Skip to content

Advertisement

You're viewing the new version of our site. Please leave us feedback.

Learn more

BMC Pregnancy and Childbirth

Open Access
Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

Validation of the Spanish version of mackey childbirth satisfaction rating scale

  • Pablo Caballero1Email author,
  • Beatriz E. Delgado-García2,
  • Isabel Orts-Cortes3,
  • Joaquin Moncho1,
  • Pamela Pereyra-Zamora1 and
  • Andreu Nolasco1
BMC Pregnancy and ChildbirthBMC series – open, inclusive and trusted201616:78

https://doi.org/10.1186/s12884-016-0862-7

Received: 5 November 2015

Accepted: 5 April 2016

Published: 16 April 2016

Abstract

Background

The “Mackey Childbirth Satisfaction Rating Scale” (MCSRS) is a complete non-validated scale which includes the most important factors associated with maternal satisfaction. Our primary purpose was to describe the internal structure of the scale and validate the reliability and validity of concept of its Spanish version MCSRS-E.

Methods

The MCSRS was translated into Spanish, back-translated and adapted to the Spanish population. It was then administered following a pilot test with women who met the study participant requirements. The scale structure was obtained by performing an exploratory factorial analysis using a sample of 304 women. The structures obtained were tested by conducting a confirmatory factorial analysis using a sample of 159 women. To test the validity of concept, the structure factors were correlated with expectations prior to childbirth experiences. McDonald’s omegas were calculated for each model to establish the reliability of each factor.

The study was carried out at four University Hospitals; Alicante, Elche, Torrevieja and Vinalopo Salud of Elche. The inclusion criteria were women aged 18–45 years old who had just delivered a singleton live baby at 38–42 weeks through vaginal delivery. Women who had difficulty speaking and understanding Spanish were excluded.

Results

The process generated 5 different possible internal structures in a nested model more consistent with the theory than other internal structures of the MCSRS applied hitherto. All of them had good levels of validation and reliability.

Conclusions

This nested model to explain internal structure of MCSRS-E can accommodate different clinical practice scenarios better than the other structures applied to date, and it is a flexible tool which can be used to identify the aspects that should be changed to improve maternal satisfaction and hence maternal health.

Keywords

Childbirth satisfactionLaborSurvey researchValidationPsychometric behaviorReliability

Background

The outcomes of health care delivery are measured in terms of effectiveness and efficiency but also in terms of the individual’s experience as a patient. This experience involves pain, autonomy, a feeling of physical and mental well-being and satisfaction with the favorable results achieved [1], and provides a unique opportunity to better understand satisfaction with the quality of the health care provided [2]. Satisfaction with health care delivery is significantly associated with patients’ adherence to medical treatment [3], their quality of life [4] or simply improvements in their health status [5]. Therefore, patients’ experiences are increasingly being used internationally as an indicator of the quality and performance of health systems [6], and thousands of surveys are used by health care providers, administrators or policymakers to assess the quality of care, make decisions about provisions and organization of health care services, avoid malpractice and support a competitive edge in the health care area [7].

Patient satisfaction is a multi-dimensional concept that has received widespread research attention since the 1970s, and it has been evaluated from different points of view and with different goals [8], but without a measuring standard [9]. According to Weisman and Koch, patient satisfaction is only measurable by direct interaction. Consequently, many patient satisfaction surveys are designed specifically for each health service [10].

The field of obstetrics is not exempt from this trend. Maternal satisfaction (MS) after childbirth has consequences for the mother’s health and the well-being of her child [11], and a measure or index of MS provides a valuable outcome to improve the quality of maternity care services [12]. Although a low Apgar score, maternal or infant mortality rates and cesarean and instrumental delivery rates have been used to assess the outcomes of maternity care services, these are very restrictive parameters for assessing quality as they do not describe attitudes or processes [1316]. In contrast, an assessment of MS with maternity care services makes it possible to determine the mother’s experience during childbirth and measure the quality of the care provided, because such care is centered on the patient’s needs [1, 12]. As a result, MS has become one of the most widely used indicators nowadays [11].

Various researchers have highlighted six main factors associated with MS besides demographic features; pain and relief, self-control, self-efficacy, expectations, partner support, and provision of opportunity to have immediate contact with the newborn [11, 12, 1724].

Several questionnaires and scales have been created in multiple languages to measure the relation between MS and the childbirth experience [25], for instance the “Maternal Well-being in Childbirth Scale” [22], the “Women’s Views of Birth Labour Satisfaction Questionnaire” [17], the “Care in Obstetrics: Measure For Testing Satisfaction Scale” [23], the “Questionnaire Measuring Attitudes About Labor and Delivery” and the “Mackey Childbirth Satisfaction Rating Scale” (MCSRS) [11]. Of these, the latter (the MCSRS) is the most complete scale as it includes the most important factors associated with MS from our point of view. Created and used in the USA, the MCSRS has also been used in other countries such as the UK [26], Holland [27, 28], Belgium and Spain [29]. The MCSRS has 34 self-report items, all of which use a 5-point Likert scale from “very dissatisfied” to “very satisfied”, and it is aimed at mothers after a vaginal delivery. According to the authors, the MCSRS consists of six subscales: general satisfaction (three items), satisfaction with self (nine items), baby (three items), midwife (nine items), gynecologist (eight items), and partner (two items) [11]. Although all of the authors who have used the MCSRS have tested the internal consistency of the MCSRS and its subscales by means of Cronbach’s alpha, none of them have described or explored its factorial structure in depth, nor have they validated the psychometric behavior of its subscales. The only exploratory factorial analysis (EFA) of the MCSRS, which was carried out by Mas-Pons et al., concerned an adaptation to Spanish with two additional questions [29]. They used principal components analysis assuming continuous variables, but better statistical tools are now available that explicitly incorporate Likert scales [30]. In addition, no confirmatory factor analysis (CFA) has been performed on the subscales proposed by the authors of the MCSRS or the researchers who have used it. EFA and CFA analyses of the scale will contribute to a better use of the MCSRS and interpretation of the results of the subscales.

Defining the dimensions underlying the MCSRS and measuring the importance that women who have just delivered a baby give to each dimension would provide a better understanding of issues related to health care delivery, enabling practitioners to introduce changes that improve the experience of childbirth and thus improve the health of mothers and their newborns.

The primary goal of the present study was to describe the internal structure of the MCSRS by means of EFA and CFA, and to examine validity of concept and reliability.

Methods

Participants

Inclusion criteria were women aged 18–45 years old who had just delivered a singleton live baby at 38–42 weeks through vaginal delivery. Women who had difficulty speaking and understanding Spanish were excluded. For the CFA, women who had undergone an unplanned cesarean section were also included to expand the population to whom the questionnaire can be administered. Participants were informed of the nature of the study and assured that the confidentiality of their personal data would be maintained. All subjects gave their written informed consent.

The study sample was recruited at the Main University Hospital of Alicante, the Main University Hospital of Elche, the University Hospital of Torrevieja, and the University Hospital Vinalopó Salud of Elche. Together, these four hospitals covered the health needs of more than half a million people in 2010. The study was reviewed and approved by the ethics committees at the four participant hospitals.

Measures

The MCSRS was translated and adapted in accordance with previous recommendations [31, 32]. It was translated into Spanish separately by two English-Spanish bilingual translators with a background in medical and health care texts and clinical experience. These two translations were used to reach an agreement resulting in the first translation into Spanish. Two other English-Spanish bilingual people, both native English speakers, translated this first translation back into English, after which a second version in Spanish was agreed upon. The definitive version was achieved after contrasting the opinions of four women who met the study participant requirements. The order and wording of the questions are shown in Table 1.
Table 1

Psychometrics factors by exploratory analysis factorial and theoretical model by Goodman et al. Rotated loading matrix to models from two to six factors. N = 304

Item

Question

2 F

RLM

3 F

RLM

4 F

RLM

5 F

RLM

6 F

RLM

T

Q1

Your overall labor experience

F1

0.623

F1

0,585

F5

0,715

F7

0.412

F7

0,383

T1

Q2

Your overall delivery experience

F1

0.569

F1

0,589

F5

0,563

F8

0.905

F8

0,952

T1

Q3

Your level of participation in decision-making during labor

F1

0.729

F1

0,664

F5

0,622

F7

0.364

F7

0,350

T2

Q4

Your level of participating in decision-making during delivery

F1

0.634

F1

0,637

F5

0,625

F8

0.544

F8

0,561

T2

Q5

Your ability to manage your labor contractions

F1

0.791

F1

0,789

F5

0,777

F7

0.772

F7

0,745

T2

Q6

Your level of comfort during labor

F1

0.638

F1

0,578

F5

0,633

F7

0.477

F7

0,450

T2

Q7

Your level of comfort during delivery

F1

0.635

F1

0,612

F5

0,564

F8

0.874

F8

0,919

T2

Q8

The control you had over your emotions during labor

F1

0.798

F1

0,845

F5

0,874

F7

0.801

F7

0,776

T2

Q9

The control you had over your emotions during delivery

F1

0.841

F1

0,891

F5

0,811

F8

0.660

F8

0,689

T2

Q10

The control you had over your actions during labor

F1

0.737

F1

0,779

F5

0,831

F7

0.909

F7

0,901

T2

Q11

The control you had over your actions during delivery

F1

0.858

F1

0,875

F5

0,758

F8

0.533

F8

0,548

T2

Q12

Your partner’s help and support during labor

F1

0.414

F1

0,421

F6

0,560

F6

0.624

F9

0,831

T6

Q13

Your partner’s help and support during delivery

F1

0.461

F1

0,448

F6

0,734

F6

0.755

F9

0,885

T6

Q14

Your baby’s physical condition at birth

F1

0,210

F1

0,236

F6

0,522

F6

0.487

F10

0,335

T3

Q15

The amount of time which passed before you first held your baby

F1

0.357

F1

0,354

F6

0,710

F6

0.654

F10

0,605

T3

Q16

The amount of time which passed before you first fed your baby

F1

0.378

F1

0,34

F6

0,593

F6

0.550

F10

0,728

T3

Q17

The physical care you received from the nursing staff during labor and delivery

F2

0.612

F3

0,882

F3

0,880

F3

0.862

F3

0,860

T4

Q18

The physical care you received from the medical staff during labor and delivery

F2

0.969

F4

0,738

F4

0,727

F4

0.727

F4

0,698

T5

Q19

The technical knowledge, ability, and competence of the nursing staff in labor and delivery

F2

0.670

F3

0,761

F3

0,724

F3

0.707

F3

0,696

T4

Q20

The technical knowledge, ability, and competence of the medical staff in labor and delivery

F2

0.876

F4

0,633

F4

0,621

F4

0.620

F4

0,631

T5

Q21

The amount of explanation or information received from the nursing staff in labor and delivery

F2

0.613

F3

0,845

F3

0,816

F3

0.826

F3

0,822

T4

Q22

The amount of explanation or information received from the medical staff in labor and delivery

F2

0.845

F4

0,654

F4

0,643

F4

0.646

F4

0,702

T5

Q23

The personal interest and attention given you by the nursing staff in labor and delivery

F2

0.775

F3

0,971

F3

0,933

F3

0.937

F3

0,935

T4

Q24

The personal interest and attention given you by the medical staff in labor and delivery

F2

0.958

F4

0,899

F4

0,884

F4

0.885

F4

0,864

T5

Q25

The help and support with breathing and relaxation which you received from the nursing staff in labor and delivery

F2

0.637

F3

0,726

F3

0,690

F3

0.718

F3

0,720

T4

Q26

The help and support with breathing and relaxation which you received from the medical staff in labor and delivery

F2

0.952

F4

0,912

F4

0,897

F4

0.904

F4

0,902

T5

Q27

The amount of time the nurses spent with you during labor

F2

0.737

F3

0,855

F3

0,855

F3

0.871

F3

0,873

T4

Q28

The amount of time the doctors spent with you during labor

F2

0.858

F4

0,89

F4

0,885

F4

0.896

F4

0,884

T5

Q29

The attitude of the nurses in labor and delivery

F2

0.620

F3

0,9

F3

0,931

F3

0.927

F3

0,924

T4

Q30

The attitude of the doctors in labor and delivery

F2

0.920

F4

1.101

F4

1,091

F4

1.096

F4

1,070

T5

Q31

The nursing staff’s sensitivity to your needs during labor and delivery

F2

0.656

F3

0,893

F3

0,872

F3

0.890

F3

0,893

T4

Q32

The medical staff’s sensitivity to your needs during labor and delivery

F2

0.995

F4

1.053

F4

1,038

F4

1.048

F4

1,051

T5

Q33

Overall, the care you received during labor and delivery

F2

0.703

F3

0,634

F3

0,611

F3

0.603

F3

0,601

T4

Q34

Overall, how satisfied or dissatisfied are you with your childbirth experience?

F1

0.419

F1

0,41

F5

0,443

F5

0.449

F5

0,480

T1

XF: model with X Factors, RLM Rotated Loading Matrix, T Theoretical Model proposed by Goodman et al. Theoretical structure T1: General satisfaction, T2: satisfaction with self, T3: baby, T4: midwife, T5: gynecologist, T6: partner. F1-F10 Names of factors obtained by EFA

Expectations prior to delivery were collected using a scale of 0–10 (where 0 means it did not fulfill my expectations at all, 5 means it was like I had imagined, and 10 means it was much better than I had expected).

Procedure

The study sample was recruited in the obstetrics and gynecology patient rooms at the four participant hospitals from September 2010 to February 2011 by consecutive sample. Skilled health personnel, midwives, selected all women who met the requirements. At 12 h postpartum these were given a leaflet and told that it had instructions for completion, on one side, and the questionnaire, on the other. It was self-completed voluntarily within 36 h by women who had just delivered. The written informed consent and the leaflet were collected by the same personnel after 24 h.

Analysis

The sample size selected for EFA was 10 subjects per item. This ratio, of 10:1, is recommended in the guide for validation and adaptation of an instrument [33]. A sample half this size was selected for CFA [34]. Consequently, the initial sample consisted of 510 women; 340 women for the EFA and 170 women for the CFA. Questionnaires that were not fully completed were excluded.

To determine the psychometric proprieties of the MCSRS, the software package FACTOR v9.20 was used to fit the EFAs [35, 36]. To test the appropriateness of applying a Factor Analysis, Bartlett’s sphericity test and the Kaiser-Meyer-Olkin index (KMO) were carried out. Multivariate skewness and kurtosis were measured to determine the multivariable normal distribution of the data by means of Mardia’s test.

FACTOR v9.20 used the polychoric correlation matrix to fit the models. The method used was weighted least squares and oblique rotation using Promin [37]. Five models were estimated, containing from 2 to 6 factors or dimensions. A parallel analysis based on 500 replications was conducted to suggest how many factors should be included to obtain the best model according to this analysis. To evaluate each model, different indices were calculated; the goodness of fit index (GFI), Bentler’s simplicity index (BSI), the loading simplicity index (LSI), which explains variance based on eigenvalues, and the root-mean-square residual (RMSR) [38]. The value of McDonald’s omega was calculated for each model to establish the reliability of each factor [3840]. Lastly, correlations among factors were calculated to determine the inter-factor relation.

The second sample was used to validate the models obtained by EFA and the original model proposed by the authors, and a CFA was performed for each model via the R programming language and its “laavan” library [41]. Model fit in the second sample was measured by means of several indices and tests; in absolute terms, using the chi-square test, root-mean-square error of approximation (RMSEA), standardized-root-mean-square residual (SRMSR) and goodness of fit index (GFI); in weighted terms by the number of estimated parameters using the adjusted goodness of fit index (AGFI) and parsimonious goodness of fit index (PGFI); in comparison to the baseline model using the comparative fit index (CFI), Normed Fit Index (NFI) and non-normed fit index (NNFI), and taking into account both weighting by the number of estimated parameters and comparing it with the baseline model using the parsimony normed fit index (PNFI) [42]. In addition, the overall congruence index (OCI) and factor congruence index (FCI) were calculated for each model and factor to check congruencies between models calculated by EFA and the best possible models calculated in the second sample.

In the absence of a gold standard, we considered that expectations prior to delivery were strongly related to MS [11, 12, 18, 19]. Thus, to validate the concept, we assumed that any scale that measures or assesses MS must also be associated with expectations prior to delivery. Thus, we calculated the score in each factor and in the MCSRS for each woman and we measured the lineal relationship with expectations using the Pearson correlation coefficient [17, 18].

Results

In the first 4 months, 390 women were recruited, 61 of whom did not take part in the study (15.6 %) and 15 did not complete all items (3.8 %); consequently, 304 participated in the EFA. In the following two months, 175 women were recruited, 16 of whom did not take part in the study (9.1 %) but all participants completed all items; thus, 159 participated in the CFA.

The average age of participants was 32.74 (SD ± 4.80). The predominant marital status was married (68.6 %). Planned pregnancies accounted for 71.8 % of cases, and 49 % of participants were primiparous. About half of the participants (53.9 %) had attended more than two sessions of maternal education. Nearly all women (96.8 %) had a main companion throughout most of the birthing process, and for most of them (95.4 %), this was their partner. In 65.7 % of cases, the onset of labor was spontaneous. Oxytocin was administered to 64.1 % of women at some point, and 79.1 % were attended by the same midwife throughout the birthing process. The most commonly used method of pharmacological pain relief was epidural analgesia (61.3 %). The average length of labor was 327.34 min (SD ± 217.54) and the average expulsive period was 53.86 min (SD ± 49.51).

The termination mode of delivery in the sample for AFE was: eutocic 86.2 % and instrumental delivery 13.8 %. The sample for the AFC distribution was: eutocic 67.8 %, instrumental delivery 14 %, and cesarean 18.2 %.

A high percentage (84.4 %) of newborns achieved a high Apgar score of 8 in the first minute. A similar percentage (83.5 %) of women had their first contact with the baby within the first 10 min of life, and contact lasted for the first two hours in 88.5 % of cases. In addition, 86.5 % of women also initiated breastfeeding within the first hour after delivery.

Bartlett’s sphericity test presented statistical significance (Statistic 7745.8, degree of freedom 561, p < 0.001) and the KMO index was 0.922, suggesting the need to apply an EFA. Mardia’s test showed statistical significance for multivariate kurtosis (Statistic 95.77, p < 0.001) although not for multivariate skewness; therefore, we could not assume a multivariate normal distribution and thus a principal component analysis was not applied. The parallel analysis suggested 2 or 4 factors. Table 1 shows the weights of each factor over the main item for models with 2–6 factors. The first factor in the models with 2 and 3 factors had loadings below the minimum required (0.3) in the 14th item (“Your baby’s physical condition at birth”). The rest of the factors in models with 4, 5 and 6 factors had loadings above this minimum.

The indices used to assess model fit are given in Tables 2 and 3. A GFI value of 1 indicates a perfect fit, and all models obtained over 0.95 for this index. Kelly’s criterion is used to assess the RMSR, where values near to or lower than Kelly’s criterion can be considered excellent [35]. In this case, Kelly’s criterion was 0.057 with an RMSR value of 0.074 for the 2-factor model, which decreased to 0.033 for the 6-factor model. A BSI value equal to 1 indicates maximum simplicity, and thus BSI models with fewer factors will obtain higher values than models with many more factors. However, in this case the BSI decreased slightly, obtaining 0.995 for the 2-factor model and 0.967 for the 6-factor model. The LSII is used to compare different models with each other; however, the values ranged between 0.504 for the 6-factor model and 0.579 for the 4-factor model. The explained variance based on eigenvalues increased with the number of eigenvalues or of factors in the model. Hence, the explained variance increased from 0.557 to 0.738.
Table 2

Indices exploratory factorial analysis

Number of factors

2

 

3

 

4

 

5

 

6

 

Goodness of Fit Index (GFI)

0,980

 

0,990

 

0,990

 

0,990

 

1,00

 

Root Mean Square of Residuals (RMSR)

0,074

 

0,060

 

0,050

 

0,038

 

0,033

 

Bentler’s simplicity index (BSI)

0,995

 

0,995

 

0,988

 

0,979

 

0,967

 

Loading Simplicity Index (LSI)

0,561

 

0,572

 

0,579

 

0,533

 

0,504

 

Explained Variance Based on eigenvalues

0,557

 

0,611

 

0,663

 

0,707

 

0,738

 

Reliability

Factor

 

Factor

 

Factor

 

Factor

 

Factor

 

Omega McDonal

1

0.973

1

0.932

1

0.928

1

0.898

1

0.896

 

2

0.933

2

0.960

2

0.816

2

0.901

2

0.911

   

3

0.971

3

0.959

3

0.816

3

0.865

     

4

0.971

4

0.961

4

0.768

       

5

0.972

5

0.961

         

6

0.971

Table 3

Indices exploratory factorial analysis

Correlation between Factors. Models from 2 to 6 factors

2 Factors

3 Factors

4 Factors

5 Factors

6 Factors

 

2

2

3

2

3

4

2

3

4

5

2

3

4

5

6

1

0.75

0.73

0.68

0.61

0.70

0.65

0.50

0.49

0.56

0.53

0.511

0.42

0.14

0.54

0.52

2

  

0.76

 

0.57

0.53

 

0.53

0.64

0.60

 

0.47

0.20

0.66

0.64

3

     

0.75

  

0.76

0.51

  

0.34

0.46

0.48

4

         

0.76

   

0.11

0.04

5

              

0.77

The oblique rotations yielded correlations between model factors; these correlations showed statistical significance for all models.

Table 4 gives the results of the CFA for the 2-factor and 6-factor models and for the original model proposed by the authors, as well as the reference levels used by the various indices calculated to indicate an excellent fit [42]. In terms of the null hypothesis, which stated that there was no difference between the original data and the fitted models, the chi-square test only yielded a significant result for the 2-factor model. Meanwhile, the RMSEA was zero for all models except the 2-factor model, for which it was 0.038, and the SRMSR reached the minimum for the 6-factor model (0.081) but increased to 0.131 for the 2-factor model, whereas it was 0.094 for the theoretical model. Consequently, all of them were above the reference level. None of the fitted models obtained a GFI above 0.95, but came close, from 0.894 for the 2-factor model to 0.949 for the 6-factor model, while the GFI was 0.930 for the theoretical model. Taking into account the number of parameters to estimate, all models obtained an AGFI and PGFI above 0.8 except the 2-factor model, which obtained a PGFI of 0.079. When the fitted models or theoretical model were compared with the baseline model, indices such as the CFI, NNFI and NFI showed a marked improvement, but when the number of estimated parameters was taken into account, only the 5-factor and 6-factor models obtained a PNFI above the reference level of 0.85. Regarding congruence, the models with 4, 5 and 6 factors obtained an OCI above the reference level of 0.85, but it was the 5-factor model which obtained the maximum value (0.919). However, 2 out of 6 factors in the theoretical model obtained a very low FCI; 0.385 and 0.289.
Table 4

Confirmatory Factorial Analysis

Number of factors

RL

2

3

4

5

6

T

Absolute Terms

X 2 Chi-Square

 

643,4

463.4

440.0

317.9

311.2

423.5

 (p-valor)

>0,05

0,000

0,973

0,996

1.000

1.000

0.998

 Root Mean Square Error of Approximation (RMSEA)

<0,07

0.038

0.000

0.000

0.000

0.000

0.000

 Standardized Root Mean Square Residual (SRMSR)

<0,08

0,131

0.102

0.096

0.090

0.081

0.094

 Goodness of fit Index (GFI)

>0,95

0.894

0.924

0.928

0.938

0.949

0.930

Weighted by the number of estimated parameters

 Adjusted Goodness of Fit Index (AGFI)

>0,8

0.880

0.913

0.917

0.928

0.940

0.919

 Parsimony Goodness of Fit Index (PGFI)

>0,8

0.790

0.813

0.812

0.815

0.816

0.800

Comparison to the baseline model

 Comparative Fit Index (CFI)

>0,95

0.973

1.000

1.000

1.000

1.000

1.000

 Non-Normed Fit Index (NNFI)

>0,95

0,971

1.000

1.000

1.000

1.000

1.000

 Normed Fit Index (NFI)

>0,90

0,868

0.905

0.910

0.920

0.936

0.913

Weighted by the number of estimated parameters and compared to the baseline model

 PNFI Parsimony Normed Fit Index

>0,85

0,814

0.845

0.845

0.850

0.860

0.833

Congruence Indices

 Factor

       

 1

>0,85

0.693

0.764

0.928

0.933

0.936

0.872

 2

>0,85

0.723

0.826

0.883

0.920

0.926

0.926

 3

>0,85

 

0.979

0.979

0.980

0.983

0.983

 4

>0,85

  

0.514

0.843

0.716

0.716

 5

>0,85

   

0.884

0.872

0.289

 6

>0,85

    

0.333

0.385

OCI Overall Congruence Index

>0,85

0.705

0.822

0.860

0.919

0.877

0.815

RL Reference Level by Lomax and Schumacker, and Lorenzo-Seva to Congruence Indices

The EFA generated 5 models and 10 different factors. To check the validity of concept of these factors and the MCSRS, scores were correlated with expectations prior to delivery, and the results are shown in Table 5; all but one of these correlations were significant.
Table 5

Pearson correlation among prior expectations, factor scores and MCSRS score

Factor

F1

F2

F3

F4

F5

F6

F7

F8

F9

F10

MCSRS

Models which include the Factor

2 F 3 F

2 F

3 F 4 F 5 F 6 F

3 F 4 F 5 F 6 F

4 F

4 F 5 F

5 F 6 F

5 F 6 F

6 F

6 F

 

Correlation

0,425

0,285

0,346

0,183

0,452

0,183

0,357

0,420

0,070

0,200

0.354

Significance (bilateral)

<0,001

0,001

<0,001

0,036

<0,001

0,037

<0,001

<0,001

0,428

0,020

<0,001

F1-F10 Names of factors obtained Factors by Exploratory Analysis Factorial. XF: Model with X Factors

Discussion

The results of Bartlett’s sphericity test and KMO suggested a factorial analysis and Mardia’s test ruled out multivariate normality of the data. Furthermore, the Likert scale variables indicated the use of a polychoric matrix and weighted least squares method in the factorial analysis instead of a principal component analysis. Although the parallel analysis suggested 2-factor or 4-factor models, an in-depth analysis of indices from the EFA or CFA might yield more possible models and another interpretation.

The EFA showed that the GFI was almost 1 for all models, and thus they all fitted the data with sufficient accuracy. The RMSR provides an assessment of the differences between the data and the model fit, whereby a RMSR below 0.08 indicates a good fit [42]; in our study, all models obtained RMSRs lower than 0.08.

Since the addition of factors increases the quality of the fit, although not necessarily the quality of the model, the BSI penalizes an unjustified increase in factors. However, the results show a slight decrease in the BSI as the number of factors increased. Therefore, models with more factors could be taken into account. Similarly, the LSI, another index which measures the simplicity of a model, showed the same behavior as the BSI.

The variation explained by eigenvalues started from 55.7 % for the 2-factor model, indicating a fair model. Thus, from the point of view of the explained variation, the rest of the models would be better than the 2-factor model.

Cronbach’s alpha has long been widely used as an estimate of the reliability of a psychometric test, and the author of the MCSRS [11] and all other researchers who have used the scale have calculated the Cronbach’s alpha [27, 29]. However, Cronbach’s alpha has been heavily criticized as an indicator for measuring reliability [43, 44]. Consequently, McDonald’s omega should be calculated [40] rather than Cronbach’s alpha. All factors from all models obtained fair omegas, and therefore no factor can be rejected due to a lack of reliability. The test-retest proposed by Keszei et al. would provide a more accurate indication of reliability, but was not performed in this study [45].

The various indices and tests implemented in the CFA showed good results for all models in the second sample. The chi-square test is widely used to analyze model fit, although the evidence is not conclusive [42]. In this case, only the 2-factor model did not pass the test. However, the rest of the indices used to assess the quality of the fit in absolute terms (the RMSEA, SRMSR and GFI) and in relative terms (the AGFI and PGFI) indicated that all the models analyzed were satisfactory. Similarly, the OCI and FCI congruence indices showed good results for all factors and all models. Meanwhile, the theoretical model obtained similar results to the 2-factor model.

In order to understand MS after childbirth using the fitted models, it is necessary to consider these models as a nested structure and to distinguish which items support each factor (Fig. 1). The results clearly show that if MS is explained by the 2-factor model, one of the factors could be named Family and the other one Care, where the Family factor would comprise the mother, the partner and the baby, while the Care factor would be obstetrical and gynecological care. The 3-factor model splits the Care factor into Midwife and Gynecologist, while the 4-factor model divides the Family factor into Self and Her Family. The 5-factor model might be the most interesting one; at this point, self-evaluation is chronologically divided into Labor and Delivery Period. Finally, the 6-factor model divides Her Family into the Baby and the Partner.
Fig. 1

Nested model, from 2-Factors model to 6-factors model

The original structure proposed by Goodman et al. and this nested model show some similarities and differences. The factors Midwife, Gynecologist, Baby and Partner appear in both models with the same supporting items. Nevertheless, the Overall factor does not exist in the nested model, and the original structure proposed by Goodman et al. does not distinguish between Labor and Delivery Period. We believe that Christiaens and Bracke (2009) might have obtained different results in their study entitled “Place of birth and satisfaction with childbirth in Belgium and the Netherlands” had they considered labor and delivery periods separately instead of as just one factor. In this case, they analyzed differences in MS between hospital birth (Belgium) and home birth (Netherlands). However, some of the women in the Dutch group spent the labor period at home but the delivery period at the hospital; consequently, the systems were not correctly compared and it was necessary to conduct another study to analyze this issue [28]. Regarding the Mas-Pons study (Mas-Pons R, et al. 2012), 2 more questions were added and an unsuitable statistical technique was employed, with the result that the structure of the psychometric factors obtained did not fit well in the theoretical framework of MS [29].

The most useful aspect of the nested model is that if the context of a study or the clinical setting does not differentiate between two factors, the model enables, for instance, a joint analysis of the care provided by the midwife and gynecologist, or maternal self-efficacy throughout the entire process.

Pearson’s correlation between expectations prior to delivery and the scores obtained by the factors and the MCSRS showed validity of concept for the MCSRS and all factors except one, the Baby factor, which only appears in the 6-factor model and did not present variability; 96.4 % of women obtained maximum scores. This result explains the lack of correlation.

Consequently, we conclude that the nested model proposed here yields a better and more in-depth description of MS within the theoretical framework of the MCSRS. In addition, it also allows us to identify naturally grouped factors and use this information to adapt them to the clinical setting.

Limitations

Of the questionnaires administered, 15.6 % were not completed during the first 4 months. This may have affected the results and also indicates that participants may have found the MCSRS-E a long and difficult questionnaire to complete. However, new questionnaires have recently been reported, such as the Childbirth Experience Questionnaire (CEQ). This has been validated for use in Sweden by Dencker et al. 2010 [45] and in the UK by Walker et al. 2015 [46], and may present another alternative means to evaluate MS.

Although the wording of the questionnaire in the Spanish translation is acceptable in all Spanish-speaking countries, it is possible that the MCSRS-E may nevertheless require a cultural adaptation.

Conclusion

The proposed nested model is in line with the theoretical framework. This structure can accommodate different clinical practice scenarios better than the other structures applied to date. Thus, if a particular clinical context requires that 2 or more factors be combined, this can only be achieved as indicated with the nested model if model validity is also to be maintained. However, MS is best understood by applying our clinical practice model with 6 factors, and we would recommend never using fewer than 4 factors. Therefore, this nested model is a flexible tool which can be used to identify the aspects that should be changed to improve MS and hence maternal health. In addition, the CFA inclusion criteria also encompassed women who underwent an unplanned cesarean section, extending the use of MCSRS.

Abbreviations

MCSRS: 

mackey childbirth satisfaction rating scale

MCSRS-E: 

Spanish version of mackey childbirth satisfaction rating scale

MS: 

maternal satisfaction

EFA: 

exploratory factorial analysis

CFA: 

confirmatory factor analysis

KMO: 

kaiser-meyer-olkin index

GFI: 

goodness of fit index

BSI: 

bentler’s simplicity index

LSI: 

loading simplicity index

RMSR: 

root-mean-square residual

RMSEA: 

root-mean-square error of approximation

SRMSR: 

standardized-root-mean-square residual

AGFI: 

adjusted goodness of fit index

PGFI: 

parsimonious goodness of fit index

CFI: 

comparative fit index

NFI: 

normed fit index

NNFI: 

non-normed fit index

PNFI: 

parsimony normed fit index

OCI: 

overall congruence index

XF: 

model with X Factors

RLM: 

rotated loading matrix

T: 

theoretical model proposed by Goodman et al

RL: 

reference Level by lomax and schumacker, and lorenzo-seva to congruence indices

Declarations

Acknowledgements

We are grateful to the Ministry of Heath of Spain for funding the study under the Healthcare Research Founding Carlos III (PI09/90257). The opinions expressed here are those of the study team and are not necessarily those of the Ministry of Heath of Spain.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Community Nursing, Preventive Medicine and Public Health and History of Science Health. Faculty of Health Sciences. University of Alicante
(2)
Obstetrics and Gynecology Service, University Hospital of Vinalopó
(3)
Department of Nursing, University of Alicante

References

  1. Mira JJ, Aranaz J. Patient satisfaction as an outcome measure in health care. Med Clin (Barc). 2000;114 Suppl 3:26–33.Google Scholar
  2. Bleich SN, Ozaltin E, Murray CK. How does satisfaction with the health-care system relate to patient experience? Bull World Health Organ. 2009;87(4):271–8.View ArticlePubMedPubMed CentralGoogle Scholar
  3. Agu KA, Oqua D, Agada P, Ohiaeri SI, Adesina A, Abdulkareem MH, et al. Assessment of satisfaction with pharmaceutical services in patients receiving antiretroviral therapy in outpatient HIV treatment setting. Int J Clin Pharm. 2014;36(3):636–47.View ArticlePubMedGoogle Scholar
  4. Mallinger JB, Griggs JJ, Shields CG. Patient-centered care and breast cancer survivors’ satisfaction with information. Patient Educ Couns. 2005;57(3):342–9.View ArticlePubMedGoogle Scholar
  5. Brana-Marcos B, Carrera-Martinez D, De la Villa-Santovena M, Avanzas-Fernandez S, Gracia-Corbato M, Vegas-Pardavila E. Sociodemographic factors and satisfaction related to the care received by women with breast cancer. Multicentre study Rev Calid Asist. 2012;27(1):30–7.View ArticlePubMedGoogle Scholar
  6. Salisbury C, Wallace M, Montgomery AA. Patients’ experience and satisfaction in primary care: secondary analysis using multilevel modelling. BMJ. 2010;341:c5004.View ArticlePubMedPubMed CentralGoogle Scholar
  7. Jackson JL, Chamberlin J, Kroenke K. Predictors of patient satisfaction. Soc Sci Med. 2001;52(4):609–20.View ArticlePubMedGoogle Scholar
  8. Sitzia J, Wood N. Patient satisfaction: a review of issues and concepts. Soc Sci Med. 1997;45(12):1829–43.View ArticlePubMedGoogle Scholar
  9. Gill L, White L. A critical review of patient satisfaction. Leadersh Health Serv. 2009;22(1):8–19.View ArticleGoogle Scholar
  10. Weisman E, Koch N. Special patient satisfaction issue. QRB Qual Rev Bull. 1989;15(6):166–7.PubMedGoogle Scholar
  11. Goodman P, Mackey MC, Tavakoli AS. Factors related to childbirth satisfaction. J Adv Nurs. 2004;46(2):212–9.View ArticlePubMedGoogle Scholar
  12. Hodnett ED. Pain and women’s satisfaction with the experience of childbirth: a systematic review. Am J Obstet Gynecol. 2002;186(5 Suppl Nature):S160–72.PubMedGoogle Scholar
  13. Mitchell PH, Ferketich S, Jennings BM. Quality health outcomes model. American Academy of Nursing Expert Panel on Quality Health Care. Image J Nurs Sch. 1998;30(1):43–6.View ArticlePubMedGoogle Scholar
  14. Murphy PA, Fullerton JT. Development of the Optimality Index as a new approach to evaluating outcomes of maternity care. J Obstet Gynecol Neonatal Nurs. 2006;35(6):770–8.View ArticlePubMedGoogle Scholar
  15. Sandin Bojo AK, Hall-Lord ML, Axelsson O, Uden G, Wilde LB. Midwifery care: development of an instrument to measure quality based on the World Health Organization’s classification of care in normal birth. J Clin Nurs. 2004;13(1):75–83.View ArticlePubMedGoogle Scholar
  16. Sandin-Bojo AK, Kvist LJ. Care in labor: a Swedish survey using the Bologna Score. Birth. 2008;35(4):321–8.View ArticlePubMedGoogle Scholar
  17. Smith LF. Development of a multidimensional labour satisfaction questionnaire: dimensions, validity, and internal reliability. Qual Health Care. 2001;10(1):17–22.View ArticlePubMedPubMed CentralGoogle Scholar
  18. Lally JE, Murtagh MJ, Macphail S, Thomson R. More in hope than expectation: a systematic review of women’s expectations and experience of pain relief in labour. BMC Med. 2008;6:7.View ArticlePubMedPubMed CentralGoogle Scholar
  19. Christiaens W, Bracke P. Assessment of social psychological determinants of satisfaction with childbirth in a cross-national perspective. BMC Pregnancy Childbirth. 2007;7:26.View ArticlePubMedPubMed CentralGoogle Scholar
  20. Bryanton J, Gagnon AJ, Johnston C, Hatem M. Predictors of women’s perceptions of the childbirth experience. J Obstet Gynecol Neonatal Nurs. 2008;37(1):24–34.View ArticlePubMedGoogle Scholar
  21. Williams CE, Povey RC, White DG. Predicting women’s intentions to use pain relief medication during childbirth using the Theory of Planned Behaviour and Self-Efficacy Theory. J Reprod Infant Psychol. 2008;26(3):168–79.View ArticleGoogle Scholar
  22. Uribe C, Contreras A, Villarroel L, Rivera S, Bravo P, Cornejo M. Bienestar Materno durante el proceso de Parto: desarrollo y aplicación de una escala de medición. Rev Chil Obstet Ginecol. 2008;73(1):4–10.View ArticleGoogle Scholar
  23. Janssen PA, Dennis CL, Reime B. Development and psychometric testing of The Care in Obstetrics: Measure for Testing Satisfaction (COMFORTS) scale. Res Nurs Health. 2006;29(1):51–60.View ArticlePubMedGoogle Scholar
  24. Ford E, Ayers S, Wright DB. Measurement of maternal perceptions of support and control in birth (SCIB). J Womens Health (Larchmt). 2009;18(2):245–52.View ArticleGoogle Scholar
  25. Sawyer A, Ayers S, Abbott J, Gyte G, Rabe H, Duley L. Measures of satisfaction with care during labour and birth: a comparative review. BMC Pregnancy Childbirth. 2013;13:108.View ArticlePubMedPubMed CentralGoogle Scholar
  26. Siassakos D, Clark J, Sibanda T, Attilakos G, Jefferys A, Cullen L, et al. A simple tool to measure patient perceptions of operative birth. BJOG. 2009;116(13):1755–61.View ArticlePubMedGoogle Scholar
  27. Christiaens W, Gouwy A, Bracke P. Does a referral from home to hospital affect satisfaction with childbirth? A cross-national comparison. BMC Health Serv Res. 2007;7:109.View ArticlePubMedPubMed CentralGoogle Scholar
  28. Christiaens W, Bracke P. Place of birth and satisfaction with childbirth in Belgium and the Netherlands. Midwifery. 2009;25(2):e11–9.View ArticlePubMedGoogle Scholar
  29. Mas-Pons R, Barona-Vilar C, Carregui-Vilar S, Ibanez-Gil N, Margaix-Fontestad L, Escriba-Aguir V. Women’s satisfaction with the experience of childbirth: validation of the Mackey Childbirth Satisfaction Rating Scale. Gac Sanit. 2012;26(3):236–42.View ArticlePubMedGoogle Scholar
  30. Ferrando PJ, Lorenzo-Seva U. Unrestricted item factor analysis and some relations with Item Response Theory. 2013Google Scholar
  31. Beck CT, Bernal H, Froman RD. Methods to document semantic equivalence of a translated scale. Res Nurs Health. 2003;26(1):64–73.View ArticlePubMedGoogle Scholar
  32. Casas Anguita J, Ramon Repullo Labrador J, Pereira Candel J. Measurements of quality of life related with health. Basic concepts and cultural adaptation. Med Clin (Barc). 2001;116(20):789–96.View ArticleGoogle Scholar
  33. Lomax RG, Schumaker RE. A beginner's guide to structural equation modeling. New York, NY: Routledge Academic, 2012. p.33–53.Google Scholar
  34. Lorenzo-Seva U, Ferrando PJ. FACTOR: a computer program to fit the exploratory factor analysis model. Behav Res Methods. 2006;38(1):88–91.View ArticlePubMedGoogle Scholar
  35. Lorezo-Seva U, Ferrando P. FACTOR v 9.20 "Manual Of The Program." 2012. Available at: http://psico.fcep.urv.es/utilitats/factor/.
  36. Lorenzo-Seva U. Promin: A method for oblique factor rotation. Multivariate Behav Res. 1999;34(3):347–65.View ArticleGoogle Scholar
  37. Lorenzo-Seva U. A factor simplicity index. Psychometrika. 2003;68(1):49–60.View ArticleGoogle Scholar
  38. Reckase M. Test theory: A unified treatment. Appl Psychol Meas. 2000;24(2):187–9.View ArticleGoogle Scholar
  39. McDonald Roderick P. Test theory: A unified treatment. Mahwah, NJ, US: Lawrence Erlbaum Associates Publishers, 1999.Google Scholar
  40. Rosseel Y. lavaan: An R package for structural equation modeling. J Stat Softw. 2012;48(2):1–36.View ArticleGoogle Scholar
  41. Lomax RG, Schumaker RE. A beginner's guide to structural equation modeling. New York, NY: Routledge Academic, 2012. p.73–123.Google Scholar
  42. Sijtsma K. On the use, the misuse, and the very limited usefulness of cronbach’s alpha. Psychometrika. 2009;74(1):107–20.View ArticlePubMedPubMed CentralGoogle Scholar
  43. Peters GJY. The alpha and the omega of scale reliability and validity: why and how to abandon Cronbach’s alpha and the route towards more comprehensive assessment of scale quality. European Health Psychol. 2014;16(2):56–69.Google Scholar
  44. Keszei AP, Novak M, Streiner DL. Introduction to health measurement scales. J Psychosom Res. 2010;68(4):319–23.View ArticlePubMedGoogle Scholar
  45. Dencker A, Taft C, Bergqvist L, Lilja H, Berg M. Childbirth experience questionnaire (CEQ): development and evaluation of a multidimensional instrument. BMC Pregnancy Childbirth. 2010;10(1):1.View ArticleGoogle Scholar
  46. Walker KF, Wilson P, Bugg GJ, Dencker A, Thornton JG. Childbirth experience questionnaire: validating its use in the United Kingdom. BMC Pregnancy Childbirth. 2015;15(1):1.View ArticleGoogle Scholar

Copyright

© Caballero et al. 2016

Advertisement