Evaluating women’s experiences and satisfaction with labour induction in India: a comparison of the Participant Generated Experience and Satisfaction (PaGES) Index with standard methods

Evaluating women’s experiences and satisfaction with labour induction in India: a comparison of the Participant Generated Experience and Satisfaction (PaGES) Index with standard methods | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluating women’s experiences and satisfaction with labour induction in India: a comparison of the Participant Generated Experience and Satisfaction (PaGES) Index with standard methods Avni Patel, Rachel Howard, Brian Faragher, Jill Durocher, Beverly Winikoff, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5417470/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 May, 2025 Read the published version in BMC Pregnancy and Childbirth → Version 1 posted 4 You are reading this latest preprint version Abstract Background Although induction of labour is becoming more common worldwide, there are few studies that assess women’s satisfaction with it. The newly developed Participant Generated Experience and Satisfaction (PaGES) Index collects brief qualitative data and quantifies it, allowing detailed satisfaction data to be collected on large populations. The PaGES data has never previously been compared to other methods of assessing study participants’ satisfaction. We aimed to triangulate PaGES Index, Likert questionnaire and interview data from a large, randomised trial of labour induction to fully understand women’s priorities, experience and satisfaction and to compare the findings of the three instruments. Methods A convergent parallel multi-methods research design was used. Participants in the Misoprostol or Oxytocin for Labour Induction (MOLI) trial (n=520) completed the PaGES Index before and after birth, listing priorities and allocating spending points to demonstrate their relative importance. Postnatally, participants scored their satisfaction with each item. Quantitative data was collected postnatally on the acceptability of augmentation, delivery time, pain and anxiety using a Likert scale. Semi-structured interviews were also conducted, and thematic analysis was carried out using a framework approach. The data from 20 participants who had completed all three outcome measures were integrated and compared. Results Although common themes, such as pain, emerged from participants’ responses to the three instruments, each provided different insights. The Likert responses demonstrated overall satisfaction with the induction process but with high levels of pain and anxiety. Semi-structured interviews highlighted that safety and health of the baby was a key priority. The PaGES Index confirmed that the baby’s wellbeing was most important to women, but women also expressed a strong preference for vaginal delivery. Conclusions The PaGES Index, Likert questionnaire and semi-structured interview data provide varied but complimentary insights on women’s birth experiences and their satisfaction with their induction process. The outputs of the three methods align, but the PaGES index was unique in capturing both detailed qualitative and quantitative information for all study participants. Trial registration The MOLI study is registered in ClinicalTrials.gov (NCT03749902, Registration date: 21 st Nov 2018) and Clinical Trial Registry, India (CTRI/2019/04/018827) Participant experience satisfaction patient-generated qualitative research quantitative research induction of labour instrument birth patient reported experience measure patient reported outcome measure Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Evaluating patient experience and satisfaction in clinical trials is vitally important and can be achieved through quantitative, qualitative and combined qualitative and quantitative instruments (1). This paper evaluates all three approaches within a multicentre randomised controlled trial (RCT) of labour induction methods in India. Induction of labour (IOL), defined as “the process of stimulating the uterus artificially to start labour” (2), is common in India (3, 4). Research conducted in high income settings has shown that women undergoing IOL generally express lower satisfaction (5). Many women feel anxious about the potential impact of IOL on their or their baby’s health, often reporting feelings of helplessness and disappointment (6). However, less than 5% of studies in a network meta-analysis of IOL methods reported data concerning maternal satisfaction (7). A systematic review identified 36 existing tools for evaluating birth experience and satisfaction (8). Of these, only seven were found to be sufficiently reliable. The Wijma Delivery Expectancy/Experience Questionnaire rated highest for both reliability and validity (9). However, no tool was able to provide both qualitative and quantitative data. Another review identified nine questionnaires (10) with two standard questionnaires, Perceptions of Care Adjective Checklist (11) and Six Simple Questions (12), recommended as potentially useful tools for comparing satisfaction at various time points (see Appendix 1). The MOLI RCT recruited 1,033 women undergoing induction for hypertensive disorders of pregnancy. The study compared the effectiveness of using a low dose oral misoprostol regimen for cervical ripening and augmentation after artificial rupture of membranes with the standard protocol of intravenous oxytocin following cervical ripening with oral misoprostol (13). The study’s aims encompassed the assessment of the protocols' safety, efficacy and acceptability, showing comparable results between the two methods. The study collected data on women’s satisfaction in two ways: a Likert scale satisfaction questionnaire and a novel patient-reported outcome measure (PROM), developed from the Patient-Generated Index (14), named the Participant-Generated Experience and Satisfaction (PaGES) Index (15). An alongside qualitative sub-study involving semi-structured interviews with a sample of participants was also conducted (full interview data will be published separately). This paper reports the satisfaction of induction of labour in the MOLI study and compares the three methods of data collection to determine the optimum method for understanding women's priorities, experiences, and satisfaction in IOL trials Methods 1033 women requiring IOL were recruited into the trial across three hospitals in Maharashtra state in India: Government Medical College in Nagpur, Mahatma Gandhi Institute of Medical Sciences in Sevagram, and Daga Memorial Hospital in Nagpur. Informed consent was sought once the decision for induction was made. All the women were initially given low-dose oral misoprostol for cervical ripening, then the 520 women who required additional augmentation were randomised to oral misoprostol or intravenous oxytocin (figure 1). Participants and their babies were followed up until hospital discharge. The Likert scale questionnaires, postnatal participant interviews and postnatal PaGES Index data were generally collected in Hindi or Marathi by a trained research associate (RA). The Likert scale responses were entered into the online data collection tool. The interviews were recorded, transcribed and translated, whilst the PaGES responses were recorded on paper in Hindi or Marathi for later translation into English. The results were integrated through a convergent parallel multi-methods design, whereby data was collected and analysed independently, and the results interpreted together. The data from participants who completed all three tools were synthesised in a summary table. Key findings and overlapping areas between the three outcome measures were also displayed diagrammatically. Likert scale questions Four Likert questions were incorporated in the MOLI post-delivery follow-up questionnaire administered by research assistants within 24 hours of the birth. Women were asked to rate the acceptability of augmentation and delivery time, as well as the amount of pain and anxiety on 5-point scales. To allow for comparison to the PaGES postnatal satisfaction scores, the pain and anxiety scales were inverted and given values from 0 to 10, with 10 representing highest satisfaction and 0 lowest satisfaction (table 1). Table 1. Likert scale questions asked during exit interviews and response codes. Domain assessed Question Likert scale (value given for comparative scoring purposes) Augmentation Please ask the woman to rate the acceptability of the augmentation method Very acceptable (10) Acceptable (7.5) Neutral (5) Unacceptable (2.5) Very unacceptable (0) Delivery time Please ask the woman to rate the acceptability of the time taken for her delivery Very acceptable (10) Acceptable (7.5) Neutral (5) Unacceptable (2.5) Very unacceptable (0) Pain Please ask the woman to rate the amount of pain experienced during her induction and delivery None (10) Slight (7.5) Moderate (5) High (2.5) Extreme (0) Anxiety Please ask the woman to rate the amount of anxiety experienced during her induction and delivery None (10) Slight (7.5) Moderate (5) High (2.5) Extreme (0) qMOLI study Semi-structured interviews were conducted with 53 women enrolled in the MOLI study either before IOL, postnatally or both. Postnatal interviews were carried out 1-6 days postpartum and women were asked questions such as; Can you explain what things are important to you about this process? Of these things (mentioned in last question), what is the most important thing? Is there anything that you feel particularly positive/happy about this process? Interviews were conducted in Hindi or Marathi by a trained research associate (RA) and were recorded, transcribed and translated. Transcripts and translation were counter-checked by a second RA. Thematic analysis of interviews from patients randomised in the MOLI study (n=20) was carried out in NVivo 20 using a framework approach (17). Following familiarisation with the interview transcripts, the data was openly coded to a working analytical framework using quotes from patient responses. The data was thoroughly reviewed and compared to the main qMOLI dataset to generate overall themes. PaGES Index 519 women recruited into the MOLI randomised trial also completed the PaGES Index after birth alongside the Likert questionnaire. Women listed up to 10 priorities and allocated 20 spending points between their priorities to demonstrate their relative importance (figure 2). To help women understand this concept, 20 cooking beans were used. The PaGES tool was also administered antenatally at recruitment (data not shown). A coding framework was developed in accordance with the framework approach following an internal pilot study (17). PaGES statements were inductively and independently coded using line-by-line thematic coding by one researcher and checked by a second researcher. Coding was repeated using multiple iterations of the coding framework and any discrepancies were resolved through discussion with the wider team. The finalised codes were aggregated into themes and overarching themes. Statistical analysis was carried out in SPSS 28.0.1.1. In this paper those recruited to the MOLI study are treated as a single cohort (the comparative PaGES results from the two arms will be published elsewhere). The number of women citing each theme was evaluated. As many mothers cited more than one important issue within the same theme, the total number of women citing each theme and overarching theme was calculated. The relative importance (number of allocated cooking beans) of each theme and satisfaction scores were reported as means and standard deviations. An overall PaGES score for each woman was generated by totalling the multiplied postnatal bean and satisfaction scores allocated to each of the participant’s statements. Results Likert scale questions 519 of the 520 women randomised in the MOLI trial completed the Likert questionnaire. One woman failed to complete the exit questionnaire and was excluded from analysis. Women reported overall satisfaction with the induction process and delivery. 78.5% of women said the overall augmentation process was acceptable (49.8%) or very acceptable (28.7%) and 57.1% of women said their delivery time was acceptable (44.2%) or very acceptable (12.9%). Over half of women reported high or extreme pain (58.6%) and 44.5% reported high or extreme anxiety (figure 3). qMOLI study 53 interviews were conducted pre- or post-IOL with women from a wide range of socioeconomic classes and varied educational status. Twenty interviews were carried out postnatally with women who had been randomised and administered either misoprostol (n=9) or oxytocin (n=11). For the study aims of this paper, this subgroup of interviews was analysed to understand women’s experiences, priorities and satisfaction in the MOLI trial. This cohort of qMOLI data is described in two themes. Women’s experiences of IOL and childbirth and Important areas identified by women . Women’s experiences of IOL and childbirth is split into the following subthemes: “blood pressure”, “delivery”, “environment”, “family”, “IOL process”, “knowledge”, “pain/ traas ”, “staff” and “thoughts and feelings”. Important areas identified by women encompasses: “baby’s future, “baby’s gender”, “timely birth and discharge”, “mode of birth”, “no pain/ traas ”, “own health” and “safety and health of baby”. Table 2 illustrates the coding framework and illustrative quotes from the analysis of the 20 postnatal qMOLI interviews with randomised participants. Additional quotes can be found in Appendix 2. Table 2. qMOLI coding framework. Themes and subthemes of qMOLI interview thematic analysis with illustrative quotes. Theme Subtheme Illustrative quotes Women’s experiences of IOL and childbirth Blood pressure “Only was worrying about BP” (Interview 3) Childbirth “As soon as I got there, I had to push hard. Doctor was asking me to push, I pushed hard and delivered normally.” (Interview 31) Environment “I was seeing the ladies so I was more scared. (Interview 11) Family “My family members are happy. Specially my husband is very happy.” (Interview 46) IOL process “They had given the first pill. It didn't bring the pain. Given a second pill, there was a backache. After the third pill, I had more pain.” (Interview 37) Knowledge “I don't have knowledge about it… but I heard from my friend, "I went to the hospital and I delivered normally within half an hour”. If she deliver normally then why not me?” (Interview 45) Pain or 'traas' “What a pain… the pain was so much… I was feeling it's coming every 2-3 minutes.. such a pain was there... a lot of pain, lot of pain.” (Interview 46) Staff “And then […] madam was there. She helped me a lot.” (Interview 46) Thoughts and feelings “Nervousness is there na… How will it happen, what will happen? (Interview 18) Important areas identified by women Baby's future “Yes... like I became a mother... how will be my baby? What will he do in future?” (Interview 37) Baby's gender “There was no tension, whether it's a baby girl or male.” (Interview 3) Mode of birth “Caesar don't have trouble early but afterwards there is a lot of trouble… normal delivery is good.” (Interview 8) Timely birth and discharge “Now, waiting to go home. When it will discharge (smiled).” (Interview 10) Own health “My BP should not raise.” (Interview 10) No pain or 'traas' “I was feeling that there should not be much pain.” (Interview 10) Safety and health of baby “My baby should be safe… I would have done anything for that.” (Interview 16) Women’s experiences of IOL and childbirth Women discussed nine focal areas of the IOL experience. They acknowledged that elevated blood pressure was the primary reason for induction and recognised its potential risk to their baby's health. Anxiety, perceived as a key contributor to hypertension, raised concerns among women, with some fearing the possibility of a caesarean section if blood pressure remained high. Environmental factors, including witnessing other women's struggles during childbirth , negatively influenced some participants. Fear and anxiety were prevalent emotions during the IOL process, particularly among primiparous women. Family advice and comparisons with relatives' childbirth experiences were common topics of discussion. Limited prior knowledge about IOL methods was noted, and several women were under the impression that the use of "saline" (oxytocin) would result in faster delivery. Despite apprehensions, women generally appreciated the necessity of induction for maternal and foetal safety. Some struggled to distinguish between IOL and the overall childbirth experience. Pain expectations varied, and contractions were often described as "terrible," after misoprostol was commenced. Positive relationships with staff , feeling supported and well-cared-for, and respectful interactions with healthcare providers were highlighted. Women thought/felt participation in the MOLI trial contributed to better quality care. Important areas identified by women Women identified seven key priorities during discussions. They often detailed plans for the baby's future , emphasising the importance of the baby remaining well, with considerations for feeding, care, and education. Gender preferences were mentioned by around half of the women, with few expressing disappointment, often deferring to male family members' opinions. Despite a preference for vaginal delivery, women prioritised the safe birth of their baby over the mode of birth , with positive sentiments about vaginal birth and some expressing relief after the fact. Timely birth and discharge were crucial for some, while concerns about pain and "traas" during labour were common priorities. Own health issues such as maintenance of normal blood pressure and discussions about family planning operations were also highlighted. Overall, the safety and health of the baby emerged as the foremost priority for almost all women, often evoking strong emotional responses. PaGES Index 2755 statements were made by the 519 women who completed the postnatal PaGES Index form, of which 1966 were allocated spending points (table 3). All postnatal codes were organised into subthemes and seven overarching themes. Overarching themes identified were “family”, “looking to the future”, “mother’s emotional wellbeing”, “mother’s physical health”, “perspective on the baby”, “perspective on the birth” and “miscellaneous”. “Satisfaction with normal vaginal delivery” (n=183) and “baby is healthy” (n=209) were the most frequently stated postnatal codes. Table 3. Postnatal PaGES codes. The top 10 most frequently cited postnatal PaGES codes with number and percentage of participants citing the code. Code Number of codes allocated beans (% of participants) Baby – healthy 212 (40.8%) Mode of Delivery – satisfaction with Normal Vaginal Delivery 179 (34.4%) Family – happy/good 140 (26.9%) Happy – to have baby 145 (27.9%) Postnatal – discharge 73 (14.0%) Postnatal – care for baby 124 (23.8%) Everything was good – staff 87 (16.7%) Pain – general 50 (9.6%) Gender – important 88 (16.9%) Long-term future – baby 77 (14.8%) Total codes 1966 (71.4%) The importance and satisfaction scores for each theme are summarised in table 4. Statements within the “perspective on the birth” overarching theme were allocated the highest proportion of beans followed by statements relating to “perspective on the baby”. “Mother’s physical health” statements were allocated the fewest spending points (beans). Mean postnatal satisfaction levels ranged from 5.19 for “mother’s physical health” to 9.03 for “family” (figure 4). Table 4. Summary of PaGES theme results. Number of women citing each theme, mean postnatal spending point (bean) scores* for all participants who cited each overarching theme with standard deviations (s.d.) and mean satisfaction scores for each of the overarching themes displayed in order of importance. *If a woman cited an overarching theme more than once her scores have been averaged to generate a mean bean score with a numerator where n = number of women. Number (and %) of women citing the theme Importance – mean number of beans allocated (+/- SD) Satisfaction – mean score (+/- SD) Perspective on birth 431 (82.9%) 8.3 (+/- 5.31) 7.06 (+/- 2.94) Perspective on baby 358 (68.9%) 7.17 (+/- 4.29) 8.68 (+/- 2.02) Mother's emotional wellbeing 210 (40.4%) 5.08 (+/- 3.85) 7.72 (+/- 2.91) Looking to the future 396 (76.2%) 4.96 (+/- 4.49) 7.25 (+/- 2.21) Family 180 (34.6%) 4.05 (+/- 3.47) 9.03 (+/- 1.60) Mother's physical health 84 (16.2%) 1.89 (+/- 2.32) 5.19 (+/- 2.68) In the open-ended PaGES assessment, some women mentioned delivery time, pain and anxiety, concepts that were directly questioned in the Likert questionnaires. This allows direct comparison between the methodologies (table 5). The mean satisfaction scores for the 3 questions were very similar in the two instruments, although only 13-24% of women identified the concepts of time to delivery, pain and anxiety as important in the PaGES questionnaire. Table 5. Comparison of PaGES Index importance and satisfaction scores with Likert responses. The number of women citing each of these key concepts in the postnatal PaGES forms and the mean number of spending points (beans) allocated to their comments (note that concepts such as pain may be cited in various codes and if so the values have been combined). Mean satisfaction scores for the codes or group of codes denoting each concept are provided and these can be directly compared to the mean Likert scores for these areas. Satisfaction Questions (Likert scale) PaGES Index Satisfaction Score – Mean Likert score (+/- SD) Number (and %) of women citing each concept Importance – mean number of beans allocated (+/- SD) Concept satisfaction (in those who cited it) – mean score (+/- SD) Acceptability No single code or group of codes represent this 7.44 (+/- 2.32) Delivery time n=68 (13.1%) 3.13 (+/- 3.05) 6.66 (+/- 2.34) 6.11 (+/- 2.73) Pain n=124 (23.9%) 2.32 (+/- 3.03) 4.82 (+/- 2.68) 3.63 (+/- 2.61) Anxiety n=70 (13.5 %) 1.81 (+/- 2.50) 4.44 (+/- 2.68) 4.58 (+/- 2.96) Integration analyses A ‘contiguous approach’ to data integration was taken for this study (18). Integration analyses were based on the postnatal data of 20 women who completed all three tools. Table 6 demonstrates three participants’ responses (see Appendix for full table). No association was found between mean Likert scores and overall postnatal PaGES scores, however common themes emerged from the PaGES Index, Likert and qMOLI data (figure 5). Women who reported ‘high’ or ‘extreme’ Likert pain scores almost always discussed experiencing severe labour pain in their qMOLI interview. These women also generally gave a PaGES Index statement relating to pain with a low satisfaction score. For example, participant 1-1190 gave a Likert pain score of 4 (high) and reported that she “ had a lot of pain in the abdomen”. She also provided a statement relating to pain on her postnatal PaGES form which had a low satisfaction score of 3. However, there were exceptions such as participant 1-1189 who also gave a Likert pain score of 4 but in her qMOLI interview said “I had problems with BP, otherwise I did not have much trouble with labour pains” . This participant discussed delivery time in her qMOLI interview and PaGES form but did not provide a score on Likert- response form. Women did not express opinions on either augmentation method unless prompted in qMOLI interviews and PaGES statements, but Likert augmentation scores demonstrated acceptable (49.8%) or very acceptable (28.7%) induction. Most women mentioned mode of birth in PaGES and qMOLI and every woman who expressed dissatisfaction with caesarean section on the postnatal PaGES form also discussed this in their qMOLI interview. For example, participant 2-0102 said “In Caesar there is a problem” and made a PaGES statement that coded for “MOB - dissatisfaction with CS” . The PaGES index scores reveal how predetermined Likert scale questions can present false impressions. For two participants (IDs 1-1189 and 1-1190), the Likert scale responses suggest that the participant was unhappy with the birth process. This was based on questions related to time in labour, pain and anxiety. The PaGES Index however reveals that the women’s main priorities were not length of time in labour and anxiety, but their baby’s safety, the desire to have a vaginal birth and the baby’s gender, none of which were asked about in the Likert questions. Table 6. Integration of results. Sample Likert, PaGES and qMOLI responses from three of the 20 randomised participants who completed all three tools colour coded with negative (red), neutral (yellow) and positive (green) scores. Overall PaGES scores were calculated by totalling the multiplied postnatal bean and satisfaction scores from each participant’s postnatal statements. Scores for all 20 women are shown in Appendix 3. Figure 5 summarises the key findings of the postnatal PaGES Index, qMOLI interviews and Likert questionnaires, and highlights overlapping themes between participants’ responses to the three tools. Areas of convergence between the PaGES statements and qMOLI quotes included mentions of staff, mode of birth and the baby’s health and wellbeing. Acceptability of delivery time and anxiety were assessed through the Likert questionnaire and were also discussed in qMOLI interviews. Pain was a common theme across all three tools. Discussion In this clinical trial on labour induction in India, satisfaction was assessed using three methods and the data compared. Analysis of standard, closed Likert questions revealed that women were generally satisfied with the IOL process with most women reporting the process of augmentation was acceptable (79%), as well as the time it took to deliver (58%). Many, however, stated high levels of pain and anxiety (59% and 45%). Pain, anxiety and fear were also themes from the 20 semi-structured postnatal interviews, but women revealed that the most important concept for them was the safety and health of the baby, and that this took priority over induction method, mode of birth and mother’s own health. Other key areas from the interviews were blood pressure, birth experience, pain and the care given by staff. However, following childbirth, women were happy and pleased to have a baby. Like the interviews, the PaGES results found that having a healthy baby was the main priority for women before and after birth. In addition, women highlighted mode of birth as a priority and expressed a clear preference for vaginal birth. “Perspective on the birth” comments were allocated the highest proportion of beans whilst “mother’s physical health” statements received the fewest beans. Women expressed lowest satisfaction with “mother’s physical health” and highest satisfaction with “family”. Overall satisfaction in the MOLI study was high, however the PaGES index revealed that it was lower in the specific areas that the women raised as important. The PaGES satisfaction scores and Likert scores yielded similar results for delivery time, pain and anxiety, however of these, only pain was raised as one of the most important issues for women in the postnatal PaGES form. A major strength of this study was the large study population and high response rate, which increased the validity of findings and allowed for meaningful comparison between the methodologies. However, the small number of interviews means that direct comparisons between the three methodologies was limited to the 20 women in the qualitative study. It was possible however to directly compare the Likert and PaGES results and to compare overall conclusions on satisfaction from each method. Each of the three methods has strengths and weaknesses. Likert scale questionnaires are quick, efficient and easy to understand measures of satisfaction that can easily be incorporated into large studies ( 19 ). As quantitative results are collected on all women within the study, interpretation and statistical analysis are rapid in order to draw conclusions and identify trends. However, the closed questions are predetermined by the research team, and they do not, as seen clearly in this study, necessarily reflect the priorities of the participants. The research team’s choice of questions (on pain, anxiety, length of time in labour, and the acceptability of the augmentation method) reflect their perceptions of what might vary between the two arms of the study, not women’s overall satisfaction. To the casual reader it therefore gives a distorted view of women’s satisfaction. Furthermore, Likert scales have their own methodological challenges including central tendency bias, as participants often avoid using extreme response categories. This was observed in the present study. Response bias may also occur when patients do not feel comfortable expressing their honest response or are reluctant to express negative views to the questioner ( 20 ). As only four Likert scale questions were included, we were only able to ascertain limited information regarding patient satisfaction. Another limitation was the lack of synonymity between Likert scales used. Two questions asked participants to rate the acceptability of their augmentation and delivery time on a scale ranging from very acceptable to very unacceptable, whilst the other two asked patients to rate their pain and anxiety on a scale of none to extreme. Thus, scores could not be assimilated to generate an overall score. Finally, we used a 5-point Likert scale. However, a 4-point scale may have been more effective as this would eliminate the possibility of selecting a neutral or moderate response ( 21 ). Qualitative interviews are a standard method to collect in-depth data on the experiences of study participants. However, the time-consuming nature of both interviews and analysis limits the sample size to when ‘data saturation’ has been reached – that is when no new information is being revealed in interviews. This allows the research team to understand the breadth of issues raised by participants, but not their frequency. It is difficult therefore to use the results to generalise about the satisfaction of the participants – only to describe the range of their experiences. This is in direct contrast to the Likert and PaGES results where the opinion of every participant was reported and frequency could be ascertained. There are additional limitations of the qualitative methodology, mainly centred around transcription and translation errors, though the team attempted to mitigate potential bias from inaccuracies through group discussion. A major strength of using the PaGES Index in the present study was that all women could be sampled, which allowed for inter-group statistical comparison. The qualitative element of the PaGES Index allowed women to freely give their opinions, whilst the quantitative component facilitated statistical analysis. However, there remained some data complexities to deal with such as bean counts not adding up to 20 and duplicated statements, both of which resulted in minor inaccuracies. Furthermore, there was ambiguity in the coding of certain statements, for example where two potential codes would have been appropriate for one statement. In these instances, a code was allocated based on group consensus, but we found that both codes usually amalgamated into the same, higher level overarching theme regardless. Overall, the PaGES Index was an easier data collection method to incorporate into the trial than interviews, which had to be scheduled separately. It also provides a more detailed insight into women’s experiences and satisfaction than a purely quantitative method such as Likert questionnaires. Furthermore, though the PaGES Index was used in a birth setting in this study, this PROM could be adapted to other trials and study populations. Post data-collection processes, including translating, data cleaning, coding and recoding, are very time consuming and complex. To further simplify the analysis, future studies might consider only implementing the PaGES Index post- intervention. Alternatively, researchers could limit the number of statements made by each participant to 5, which would still be sufficient to capture the participants’ views. It may also be more practical for participants to select codes from an extensive predetermined list once an initial PaGES dataset for a clinical situation and setting has been obtained and coded. Comparison with other studies As the PaGES Index was pioneered in this study, there have been no previous published comparisons with other methodologies. Furthermore, virtually all previous qualitative research on birth outcomes, whether induced or not, comes from high income settings. It may seem surprising therefore that our satisfaction findings are consistent with much of the existing quantitative research of women’s experience of IOL. Generally, studies assessing satisfaction through Likert scale questions report overall positive birth experiences. A high-income setting study assessing maternal satisfaction with induction using a 10-point scale found that women in both the treatment and placebo groups expressed overall satisfaction following induction and birth ( 22 ). Qualitative interviews with women however reveal many negative experiences, often due to receiving insufficient information and issues surrounding decision-making, support and environment ( 23 ). Dissatisfaction with IOL is associated with a lack of knowledge ( 24 ), and women are often “surprised” to find they need to be induced and experience stress whilst awaiting active labour ( 25 ). Researchers therefore conclude that women should be kept updated at each step of their induction process and healthcare professionals should ensure women are aware about the potential need for additional interventions and pain management ( 26 ). Fear of childbirth before labour is a risk factor for a negative experience of IOL. Many women reported high levels of pain following induction, a finding that is echoed by other qualitative studies ( 27 , 28 ). However, no systematic reviews have synthesised evidence relating to women's experiences of pain with various induction methods. Women describing their experiences of IOL often mentioned care from staff, demonstrating that women undergoing IOL can have a positive birth experience if well supported ( 29 ). Implications Considerable understanding of the priorities, experience and satisfaction of women in the MOLI study has been gained as a result of the triangulated analysis of the PaGES Index, Likert questionnaire and qMOLI interviews results. Each of the three methods has its own strength and a role to play in assessing birth satisfaction. Likert scales provide rapid data that is easy to assess and analyse, and can be useful if research funds are limited. However, the results need to be viewed with caution as they only reflect the priorities of the research team and do not explore the range or relative importance of participants’ views. Qualitative data generated through semi-structured interviews is time consuming to collect and analyse, and can only tell of the breadth of experience, not their relative frequency. They cannot therefore be used to compare two randomised groups or to describe the frequency of the experiences. They do, however, effectively explore the depth of the participants’ experiences and help to explain the quantitative data. They are best used alongside quantitative analyses. The new PaGES Index seeks to provide the ‘best of both worlds’ with an initial rapid qualitative assessment using open questions, followed by a quantification of both the relative importance and satisfaction of each item generated. Conclusions The PaGES Index, Likert questionnaires and semi-structured interviews provide varied data which are difficult to compare directly. The Likert questions only asked about four specific areas, but sample all randomised participants. Conversely, the interviews asked open questions about a range of topics but to a limited sample of women. The PaGES index is a hybrid measure which asks brief open questions about priorities to all women, then allows women to quantify their relative importance and satisfaction with each. This allows highly granular data to be collected and quantified from all participants. This study shows that the PaGES results align with both Likert and interview results. We therefore conclude that the PaGES Index is a feasible PROM for collecting detailed qualitative and quantitative insights. Future research should further validate the PaGES Index in other trials and study populations, including among groups receiving care for other health conditions. Declarations Ethics approval and consent to participate This trial is sponsored by the University of Liverpool (Brownlow Hill, Liverpool L69 7ZX, UK; UoL001374) which oversees the study quality and has final responsibility for the study conduct. The study was approved by the Institutional Ethics Committees at Government Medical College Nagpur (1724 EC/Pharmac/GMC/NGP), Spandan Heart Institute and Research Center (MOLI Study), the Mahatma Gandhi Institute of Medical Sciences (MGIMS/IEC/OBGY/96/2020) and the University of Liverpool (UoL001374). The study is insured by the sponsor (for harm arising from protocol design) and by the recruiting sites (for clinical negligence). The MOLI study is registered with ClinicalTrials.gov (NCT03749902, Registration date: 21 st Nov 2018) and Clinical Trial Registry, India (CTRI/2019/04/018827). All women enrolled in the trial provided informed written consent. Consent for publication Not applicable. Availability of data and materials Until full results are published, only the study investigators will have access to the data files. Subsequently, the full database, including the PaGES Index data, will be made available to other researchers upon request. Efforts will also be made to use open access databases to ensure our data is widely accessible with minimal restrictions, adhering to MRC and Wellcome Trust guidelines. Competing interests All authors declare that they have no competing interests. Funding Funding was provided by the Department of Health and Social Care (DHSC), the Foreign, Commonwealth & Development Office (FCDO), the Medical Research Council (MRC) and Wellcome through the Joint Global Health Trials scheme (MR/R006/180/1) and included external peer review. The funder attended Trial Steering Committee meetings, but otherwise played no part in the conduct of the research or writing the paper. Authors contributions AW led the grant application, chaired the trial management group and is the guarantor of the MOLI study. SM was the lead investigator in India. AS, KL, BF and BW were also involved in the planning and preparation of the MOLI RCT. Data collection was overseen by SM, JD and KL. The PaGES index data was coded by RH and AP. The data analyses presented in this paper were carried out by AP with input from BF. All authors had full access to all the data in the study and contributed to the checking of the final manuscript. Acknowledgements We thank the MRC, Wellcome Trust, NIHR and UK government for providing funding for the study through the Joint Global Health Trials scheme. We also thank Moushmi Tadas, Seema Parvekar, Poonam Varma Shivkumar and the research teams who ran the MOLI study in India and collected the data that made this secondary analysis possible. References Lavela S, Gallan A. Evaluation and measurement of patient experience. Patient Exp J. 2014;1(1):28-36. World Health Organization UNPF, UNICEF. Managing Complications in Pregnancy and Childbirth: A guide for midwives and doctors 2017 [cited 2020 24 May]. Available from: https://apps.who.int/iris/bitstream/handle/10665/255760/9789241565493-eng.pdf;jsessionid=0A0BF303B9D76469B230A0ACB8CF8440?sequence=1. Nagpal J, Sachdeva A, Sengupta Dhar R, Bhargava VL, Bhartia A. Widespread non-adherence to evidence-based maternity care guidelines: a population-based cluster randomised household survey. British Journal of Obstetrics and Gynaecology. 2015;122(2):238-47. Vogel JP, Souza JP, Gülmezoglu AM. Patterns and Outcomes of Induction of Labour in Africa and Asia: a secondary analysis of the WHO Global Survey on Maternal and Neonatal Health. PloS one. 2013;8(6):65612. Henderson J, Redshaw M. Women's experience of induction of labor: a mixed methods study. Acta obstetricia et gynecologica Scandinavica. 2013;92(10):1159-67. Gatward H, Simpson M, Woodhart L, Stainton MC. Women's experiences of being induced for post-date pregnancy. Women and birth : journal of the Australian College of Midwives. 2010;23(1):3-9. Alfirevic Z, Keeney E, Dowswell T, Welton NJ, Medley N, Dias S, et al. Which method is best for the induction of labour? A systematic review, network meta-analysis and cost-effectiveness analysis. Health Technol Assess. 2016;20(65):1-584. Nilvér H, Begley C, Berg M. Measuring women’s childbirth experiences: a systematic review for identification and analysis of validated instruments. BMC pregnancy and childbirth. 2017;17(1):203. Wijma K, Wijma B, Zar M. Psychometric aspects of the W-DEQ; a new questionnaire for the measurement of fear of childbirth. Journal of psychosomatic obstetrics and gynaecology. 1998;19(2):84-97. 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 and childbirth. 2013;13(1):108. Redshaw M, Martin CR. Validation of a perceptions of care adjective checklist. Journal of evaluation in clinical practice. 2009;15(2):281-8. Harvey S, Rach D, Stainton MC, Jarrell J, Brant R. Evaluation of satisfaction with midwifery care. Midwifery. 2002;18(4):260-7. Mundle S, Lightly K, Durocher J, Bracken H, Tadas M, Parvekar S, et al. Oral misoprostol alone, compared with oral misoprostol followed by oxytocin, in women induced for hypertension of pregnancy: A multicentre randomised trial. BJOG: An International Journal of Obstetrics & Gynaecology. 2024;n/a(n/a). Ruta DA, Garratt AM, Leng M, Russell IT, MacDonald LM. A new approach to the measurement of quality of life. The Patient-Generated Index. Medical care. 1994;32(11):1109-26. Symon A, Lightly K, Howard R, Mundle S, Faragher B, Hanley M, et al. Introducing the participant-generated experience and satisfaction (PaGES) index: a novel, longitudinal mixed-methods evaluation tool. BMC Med Res Methodol. 2023;23(1):214. Bracken H, Lightly K, Mundle S, Kerr R, Faragher B, Easterling T, et al. Oral Misoprostol alone versus oral misoprostol followed by oxytocin for labour induction in women with hypertension in pregnancy (MOLI): protocol for a randomised controlled trial. BMC pregnancy and childbirth. 2021;21(1):537. Gale N, Heath G, Cameron E, Rashid S, Redwood S. Using the framework method for the analysis of qualitative data in multi-disciplinary health research. BMC Medical Research Methodology. 2013;13(1):117. Fetters MD, Curry LA, Creswell JW. Achieving integration in mixed methods designs-principles and practices. Health Serv Res. 2013;48(6 Pt 2):2134-56. Jebb AT, Ng V, Tay L. A Review of Key Likert Scale Development Advances: 1995–2019. Frontiers in Psychology. 2021;12. Westland JC. Information loss and bias in likert survey responses. PloS one. 2022;17(7):e0271949. Garland R. The Mid-Point on a Rating Scale: Is it Desirable? Marketing Bulletin. 1991;2:66-70. Bollapragada S, MacKenzie F, Norrie J, Eddama O, Petrou S, Reid M, et al. Randomised placebo-controlled trial of outpatient (at home) cervical ripening with isosorbide mononitrate (IMN) prior to induction of labour – clinical trial with analyses of efficacy and acceptability. The IMOP Study. BJOG: An International Journal of Obstetrics & Gynaecology. 2009;116(9):1185-95. Coates R, Cupples G, Scamell A, McCourt C. Women's experiences of induction of labour: Qualitative systematic review and thematic synthesis. Midwifery. 2019;69:17-28. Dupont C, Blanc-Petitjean P, Cortet M, Gaucher L, Salomé M, Carbonne B, et al. Dissatisfaction of women with induction of labour according to parity: Results of a population-based cohort study. Midwifery. 2020;84:102663. Lundh C, Øvrum AK, Dahl B. Women's experiences with unexpected induction of labor: A qualitative study. Eur J Midwifery. 2023;7:7. RCM. Midwifery care for Induction of Labour. 2019. Jay A, Thomas H, Brooks F. In labor or in limbo? The experiences of women undergoing induction of labor in hospital: Findings of a qualitative study. Birth (Berkeley, Calif). 2018;45(1):64-70. Lima B, Ribeiro MMA, Martins Rose Costa E, Conceição de Almeida Ramos R, Francisco MTR, Valério Machado de Lima D. Feelings amongst high-risk pregnant women during induction of labor: a descriptive study. Online Braz J Nurs. 2016;15:254-64. Ford E, Ayers S, Wright DB. Measurement of maternal perceptions of Support and Control in Birth (SCIB). Journal of Women's Health. 2009;18(2):245-52. Dencker A, Taft C, Bergqvist L, Lilja H, Berg M. Childbirth experience questionnaire (CEQ): development and evaluation of a multidimensional instrument. BMC pregnancy and childbirth. 2010;10(1):81-8. Van der Kooy J, Valentine NB, Birnie E, Vujkovic M, de Graaf JP, Denktaş S, et al. Validity of a questionnaire measuring the world health organization concept of health system responsiveness with respect to perinatal services in the dutch obstetric care system. BMC Health Services Research. 2014;14(1):622. Sjetne IS, Iversen HH, Kjøllesdal JG. A questionnaire to measure women’s experiences with pregnancy, birth and postnatal care: instrument development and assessment following a national survey in Norway. BMC pregnancy and childbirth. 2015;15(1):182. Truijens SE, Wijnen HA, Pommer AM, Oei SG, Pop VJ. Development of the Childbirth Perception Scale (CPS): perception of delivery and the first postpartum week. Archives of women's mental health. 2014;17(5):411-21. Additional Declarations No competing interests reported. Supplementary Files Appendix.docx Cite Share Download PDF Status: Published Journal Publication published 28 May, 2025 Read the published version in BMC Pregnancy and Childbirth → Version 1 posted Editorial decision: Revision requested 18 Nov, 2024 Editor assigned by journal 16 Nov, 2024 Submission checks completed at journal 16 Nov, 2024 First submitted to journal 08 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5417470","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":379424630,"identity":"66d8f22a-e5c7-47b4-b9c4-05dc0dc77c62","order_by":0,"name":"Avni Patel","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYBACA3YwJWHHz87YABFih8rg1MIMIhMskiWbYVqYidNSwbjhMEyIkBZzZuZnD37+kGA2PszcJvFxzx05fmYGxg8/GA4b49Ji2cxmbtiTIMFndpixTXLGs2fGks0MzJI9DIfNcDrsMIOZBE+CBDNQS7Mxz4HDiSAXSjMwHLbBrYX9m+SfBAnGzc1ALX8gWph/49fCYyYNtIVxAzNj42MGiBY2kC14HMZTbiyTJpEscZix8WHPAZBfGNssewzScXrf4Hj7todvbOrs+NvbHxz4cQAYYuzNh2/8qLA2bMClh4GBDZlzAIhBcYozVrBrGQWjYBSMglGACgBWclGx93EzXQAAAABJRU5ErkJggg==","orcid":"","institution":"University of Liverpool","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Avni","middleName":"","lastName":"Patel","suffix":""},{"id":379424631,"identity":"f1c89d74-08c0-4e34-b657-ae828a19aab7","order_by":1,"name":"Rachel Howard","email":"","orcid":"","institution":"University of Liverpool","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rachel","middleName":"","lastName":"Howard","suffix":""},{"id":379424632,"identity":"e204e8aa-56a7-4292-9d60-a985ed7dc6bf","order_by":2,"name":"Brian Faragher","email":"","orcid":"","institution":"Liverpool School of Tropical Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Brian","middleName":"","lastName":"Faragher","suffix":""},{"id":379424633,"identity":"fb72c4b0-dc3d-4f0f-9713-0d678f80639d","order_by":3,"name":"Jill Durocher","email":"","orcid":"","institution":"Gynuity Health Projects","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jill","middleName":"","lastName":"Durocher","suffix":""},{"id":379424634,"identity":"5b1e47bd-ae87-4b4f-b3e4-6acafc40c796","order_by":4,"name":"Beverly Winikoff","email":"","orcid":"","institution":"Gynuity Health Projects","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Beverly","middleName":"","lastName":"Winikoff","suffix":""},{"id":379424635,"identity":"e3d5146f-b4f1-4127-aea9-0e06d43ee668","order_by":5,"name":"Andrew Symon","email":"","orcid":"","institution":"4HJ University of Dundee","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"","lastName":"Symon","suffix":""},{"id":379424636,"identity":"44102c4e-fa10-4a76-ab8f-c02e480acc98","order_by":6,"name":"Andrew Weeks","email":"","orcid":"","institution":"University of Liverpool, Liverpool Women’s Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"","lastName":"Weeks","suffix":""},{"id":379424637,"identity":"5050e760-1862-411a-9374-ad3b1a6fa9af","order_by":7,"name":"Shuchita Mundle","email":"","orcid":"","institution":"All India Institute of Medical Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shuchita","middleName":"","lastName":"Mundle","suffix":""},{"id":379424638,"identity":"6dbd51d2-e67c-4454-b128-4f4f2ba121b1","order_by":8,"name":"Kate Lightly","email":"","orcid":"","institution":"University of Liverpool, Liverpool Women’s Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kate","middleName":"","lastName":"Lightly","suffix":""}],"badges":[],"createdAt":"2024-11-08 15:08:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5417470/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5417470/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12884-025-07731-9","type":"published","date":"2025-05-28T15:57:29+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":71801811,"identity":"edebb1c5-fc80-493a-8bfa-5d16728d5943","added_by":"auto","created_at":"2024-12-18 16:55:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":36459,"visible":true,"origin":"","legend":"\u003cp\u003eMisoprostol or Oxytocin for Labour Induction (MOLI) randomised control trial study flow adapted from MOLI study protocol (16). The grey box highlights the data used to understand women’s experience and satisfaction following induction and birth. The PaGES Index was completed by 519 of the 520 women in the MOLI RCT. \u0026nbsp;519 women in the RCT also completed the Likert scale questionnaire following birth. \u0026nbsp;A sample of women (n=20) were also interviewed 1-6 days postpartum (qMOLI protocol). \u0026nbsp;\u003cem\u003eAbbreviations: AN – antenatal, PN – postnatal, MOLI – Misoprostol or Oxytocin for Labour Induction, qMOLI – qualitative MOLI sub-study, PaGES Index – Patient Generated Experience and Satisfaction Index.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5417470/v1/51921d167692a00af13f02f1.png"},{"id":71800363,"identity":"c1f028fc-ce2c-435d-af10-6831ce7c96d0","added_by":"auto","created_at":"2024-12-18 16:39:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":145389,"visible":true,"origin":"","legend":"\u003cp\u003ePostnatal PaGES Index case report form (CRF). \u0026nbsp;Women identify up to 10 important areas post-IOL and allocate 20 spending points to demonstrate the personal importance of these areas. Step 3 allows women to rate their satisfaction with their birth experience in each of the areas they have identified as important to them.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5417470/v1/1cc4392edac3b7b636c30ce0.png"},{"id":71800541,"identity":"3778f6fb-7854-402f-a34c-16901b045aa0","added_by":"auto","created_at":"2024-12-18 16:47:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":26277,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of Likert question responses. Stacked bar charts illustrating the percentage of responses to the four Likert scale questions regarding the acceptability of augmentation, delivery time, pain and anxiety.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5417470/v1/453f9e51709052b76d660aba.png"},{"id":71800543,"identity":"e9b1e33d-8c98-4491-bfbc-4d44565ce7d1","added_by":"auto","created_at":"2024-12-18 16:47:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":34429,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePostnatal satisfaction scores.\u003c/strong\u003e Box and whisker plot of postnatal satisfaction scores for each overarching theme. Boxes represent median and IQR, whiskers represent minimum and maximum, crosses represent the mean and dots show individual outliers.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5417470/v1/8644294898a0136d3fe27b3c.png"},{"id":71800358,"identity":"f1a317c7-49e8-4e90-b665-dfb957391db5","added_by":"auto","created_at":"2024-12-18 16:39:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":134312,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntegration of PaGES, Likert and qMOLI key findings. \u003c/strong\u003eVenn diagram summarising the key findings of the three tools, demonstrating overlapping areas.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5417470/v1/1a7c94b9d12a3890bd1c147f.png"},{"id":83783057,"identity":"590fa082-7116-47de-abc4-4ee8db0888a3","added_by":"auto","created_at":"2025-06-02 16:10:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1391206,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5417470/v1/3a8a02cc-0f71-4dcc-a146-22626c71e35b.pdf"},{"id":71800362,"identity":"a5f7304b-d69c-4f47-9ce6-643c716ebe8f","added_by":"auto","created_at":"2024-12-18 16:39:21","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":90160,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-5417470/v1/d363e26a24a52ecc1cd3a568.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluating women’s experiences and satisfaction with labour induction in India: a comparison of the Participant Generated Experience and Satisfaction (PaGES) Index with standard methods","fulltext":[{"header":"Background","content":"\u003cp\u003eEvaluating patient experience and satisfaction in clinical trials is vitally important and can be achieved through quantitative, qualitative and combined qualitative and quantitative instruments (1). \u0026nbsp;This paper evaluates all three approaches within a multicentre randomised controlled trial (RCT) of labour induction methods in India.\u003c/p\u003e\n\u003cp\u003eInduction of labour (IOL), defined as “the process of stimulating the uterus artificially to start labour” (2), is common in India (3, 4). \u0026nbsp;Research conducted in high income settings has shown that women undergoing IOL generally express lower satisfaction (5). \u0026nbsp;Many women feel anxious about the potential impact of IOL on their or their baby’s health, often reporting feelings of helplessness and disappointment (6). \u0026nbsp;However, less than 5% of studies in a network meta-analysis of IOL methods reported data concerning maternal satisfaction (7).\u003c/p\u003e\n\u003cp\u003eA systematic review identified 36 existing tools for evaluating birth experience and satisfaction (8). \u0026nbsp;Of these, only seven were found to be sufficiently reliable. \u0026nbsp;The Wijma Delivery Expectancy/Experience Questionnaire rated highest \u0026nbsp;for both reliability and validity (9). \u0026nbsp;However, no tool was able to provide both qualitative and quantitative data. \u0026nbsp;Another review identified nine questionnaires (10) with two standard questionnaires, Perceptions of Care Adjective Checklist (11) and Six Simple Questions (12), recommended as potentially useful tools for comparing satisfaction at various time points (see Appendix 1).\u003c/p\u003e\n\u003cp\u003eThe MOLI RCT recruited 1,033 women undergoing induction for hypertensive disorders of pregnancy. \u0026nbsp;The study compared the effectiveness of using a low dose oral misoprostol regimen for cervical ripening and augmentation after artificial rupture of membranes with the standard protocol of intravenous oxytocin following cervical ripening with oral misoprostol (13). \u0026nbsp;The study’s aims encompassed the assessment of the protocols' safety, efficacy and acceptability, showing comparable results between the two methods. \u0026nbsp;The study collected data on women’s satisfaction in two ways: a Likert scale satisfaction questionnaire and a novel patient-reported outcome measure (PROM), developed from the Patient-Generated Index (14), named the Participant-Generated Experience and Satisfaction (PaGES) Index (15). \u0026nbsp;An alongside qualitative sub-study involving semi-structured interviews with a sample of participants was also conducted (full interview data will be published separately). \u0026nbsp;This paper reports the satisfaction of induction of labour in the MOLI study and compares the three methods of data collection to determine the optimum method for understanding women's priorities, experiences, and satisfaction in IOL trials\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e1033 women requiring IOL were recruited into the trial across three hospitals in Maharashtra state in India: Government Medical College in Nagpur, Mahatma Gandhi Institute of Medical Sciences in Sevagram, and Daga Memorial Hospital in Nagpur. \u0026nbsp;Informed consent was sought once the decision for induction was made. \u0026nbsp;All the women were initially given low-dose oral misoprostol for cervical ripening, then the 520 women who required additional augmentation were randomised to oral misoprostol or intravenous oxytocin (figure 1). \u0026nbsp;Participants and their babies were followed up until hospital discharge.\u003c/p\u003e\n\u003cp\u003eThe Likert scale questionnaires, postnatal participant interviews and postnatal PaGES Index data were generally collected in Hindi or Marathi by a trained research associate (RA). \u0026nbsp;The Likert scale responses were entered into the online data collection tool. \u0026nbsp;The interviews were recorded, transcribed and translated, whilst the PaGES responses were recorded on paper in Hindi or Marathi for later translation into English. \u0026nbsp;The results were integrated through a convergent parallel multi-methods design, whereby data was collected and analysed independently, and the results interpreted together. \u0026nbsp;The data from participants who completed all three tools were synthesised in a summary table. \u0026nbsp;Key findings and overlapping areas between the three outcome measures were also displayed diagrammatically.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLikert scale questions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFour Likert questions were incorporated in the MOLI post-delivery follow-up questionnaire administered by research assistants within 24 hours of the birth. \u0026nbsp;Women were asked to rate the acceptability of augmentation and delivery time, as well as the amount of pain and anxiety on 5-point scales. \u0026nbsp;To allow for comparison to the PaGES postnatal satisfaction scores, the pain and anxiety scales were inverted and given values from 0 to 10, with 10 representing highest satisfaction and 0 lowest satisfaction (table 1).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eLikert scale questions asked during exit interviews and response codes.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDomain assessed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuestion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLikert scale (value given for comparative scoring purposes)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eAugmentation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003ePlease ask the woman to rate the acceptability of the augmentation method\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eVery acceptable (10)\u003c/p\u003e\n \u003cp\u003eAcceptable (7.5)\u003c/p\u003e\n \u003cp\u003eNeutral (5)\u003c/p\u003e\n \u003cp\u003eUnacceptable (2.5)\u003c/p\u003e\n \u003cp\u003eVery unacceptable (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eDelivery time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003ePlease ask the woman to rate the acceptability of the time taken for her delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eVery acceptable (10)\u003c/p\u003e\n \u003cp\u003eAcceptable (7.5)\u003c/p\u003e\n \u003cp\u003eNeutral (5)\u003c/p\u003e\n \u003cp\u003eUnacceptable (2.5)\u003c/p\u003e\n \u003cp\u003eVery unacceptable (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003ePain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003ePlease ask the woman to rate the amount of pain experienced during her induction and delivery\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eNone (10)\u003c/p\u003e\n \u003cp\u003eSlight (7.5)\u003c/p\u003e\n \u003cp\u003eModerate (5)\u003c/p\u003e\n \u003cp\u003eHigh (2.5)\u003c/p\u003e\n \u003cp\u003eExtreme (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eAnxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003ePlease ask the woman to rate the amount of anxiety experienced during her induction and delivery\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eNone (10)\u003c/p\u003e\n \u003cp\u003eSlight (7.5)\u003c/p\u003e\n \u003cp\u003eModerate (5)\u003c/p\u003e\n \u003cp\u003eHigh (2.5)\u003c/p\u003e\n \u003cp\u003eExtreme (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eqMOLI study\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSemi-structured interviews were conducted with 53 women enrolled in the MOLI study either before IOL, postnatally or both. \u0026nbsp;Postnatal interviews were carried out 1-6 days postpartum and women were asked questions such as;\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eCan you explain what things are important to you about this process?\u003c/li\u003e\n \u003cli\u003eOf these things (mentioned in last question), what is the most important thing?\u003c/li\u003e\n \u003cli\u003eIs there anything that you feel particularly positive/happy about this process?\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eInterviews were conducted in Hindi or Marathi by a trained research associate (RA) and were recorded, transcribed and translated. \u0026nbsp;Transcripts and translation were counter-checked by a second RA.\u003c/p\u003e\n\u003cp\u003eThematic analysis of interviews from patients randomised in the MOLI study (n=20) was carried out in NVivo 20 using a framework approach (17). \u0026nbsp;Following familiarisation with the interview transcripts, the data was openly coded to a working analytical framework using quotes from patient responses. \u0026nbsp;The data was thoroughly reviewed and compared to the main qMOLI dataset to generate overall themes.\u003cem\u003e\u003cbr\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePaGES Index\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e519 women recruited into the MOLI randomised trial also completed the PaGES Index after birth alongside the Likert questionnaire. \u0026nbsp;Women listed up to 10 priorities and allocated 20 spending points between their priorities to demonstrate their relative importance (figure 2). To help women understand this concept, 20 cooking beans were used. \u0026nbsp;The PaGES tool was also administered antenatally at recruitment (data not shown).\u003c/p\u003e\n\u003cp\u003eA coding framework was developed in accordance with the framework approach following an internal pilot study (17). \u0026nbsp;PaGES statements were inductively and independently coded using line-by-line thematic coding by one researcher and checked by a second researcher. \u0026nbsp; Coding was repeated using multiple iterations of the coding framework and any discrepancies were resolved through discussion with the wider team. \u0026nbsp;The finalised codes were aggregated into themes and overarching themes.\u003c/p\u003e\n\u003cp\u003eStatistical analysis was carried out in SPSS 28.0.1.1. \u0026nbsp; In this paper those recruited to the MOLI study are treated as a single cohort (the comparative PaGES results from the two arms will be published elsewhere). The number of women citing each theme was evaluated. \u0026nbsp;As many mothers cited more than one important issue within the same theme, the total number of women citing each theme and overarching theme was calculated. \u0026nbsp;The relative importance (number of allocated cooking beans) of each theme and satisfaction scores were reported as means and standard deviations. \u0026nbsp;An overall PaGES score for each woman was generated by totalling the multiplied postnatal bean and satisfaction scores allocated to each of the participant\u0026rsquo;s statements. \u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003e\u003cem\u003eLikert scale questions\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003e519 of the 520 women randomised in the MOLI trial completed the Likert questionnaire. \u0026nbsp;One woman failed to complete the exit questionnaire and was excluded from analysis. \u0026nbsp; Women reported overall satisfaction with the induction process and delivery. \u0026nbsp;78.5% of women said the overall augmentation process was acceptable (49.8%) or very acceptable (28.7%) and 57.1% of women said their delivery time was acceptable (44.2%) or very acceptable (12.9%). \u0026nbsp;Over half of women reported high or extreme pain (58.6%) and 44.5% reported high or extreme anxiety (figure 3).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003eqMOLI study\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003e53 interviews were conducted pre- or post-IOL with women from a wide range of socioeconomic classes and varied educational status. \u0026nbsp;Twenty interviews were carried out postnatally with women who had been randomised and administered either misoprostol (n=9) or oxytocin (n=11). \u0026nbsp;For the study aims of this paper, this subgroup of interviews was analysed to understand women\u0026rsquo;s experiences, priorities and satisfaction in the MOLI trial.\u003c/p\u003e\n\u003cp\u003eThis cohort of qMOLI data is described in two themes. \u003cem\u003eWomen\u0026rsquo;s experiences of IOL and childbirth\u003c/em\u003e and \u003cem\u003eImportant areas identified by women\u003c/em\u003e. \u0026nbsp;\u003cem\u003eWomen\u0026rsquo;s experiences of IOL and childbirth\u003c/em\u003e is split into the following subthemes: \u0026ldquo;blood pressure\u0026rdquo;, \u0026ldquo;delivery\u0026rdquo;, \u0026ldquo;environment\u0026rdquo;, \u0026ldquo;family\u0026rdquo;, \u0026ldquo;IOL process\u0026rdquo;, \u0026ldquo;knowledge\u0026rdquo;, \u0026ldquo;pain/\u003cem\u003etraas\u003c/em\u003e\u0026rdquo;, \u0026ldquo;staff\u0026rdquo; and \u0026ldquo;thoughts and feelings\u0026rdquo;. \u0026nbsp;\u003cem\u003eImportant areas identified by women\u0026nbsp;\u003c/em\u003eencompasses: \u0026ldquo;baby\u0026rsquo;s future, \u0026ldquo;baby\u0026rsquo;s gender\u0026rdquo;, \u0026ldquo;timely birth and discharge\u0026rdquo;, \u0026ldquo;mode of birth\u0026rdquo;, \u0026ldquo;no pain/\u003cem\u003etraas\u003c/em\u003e\u0026rdquo;, \u0026ldquo;own health\u0026rdquo; and \u0026ldquo;safety and health of baby\u0026rdquo;. \u0026nbsp;Table 2 illustrates the coding framework and illustrative quotes from the analysis of the 20 postnatal qMOLI interviews with randomised participants. \u0026nbsp;Additional quotes can be found in Appendix 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. qMOLI coding framework.\u0026nbsp;\u003c/strong\u003eThemes and subthemes of qMOLI interview thematic analysis with illustrative quotes.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTheme\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubtheme\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIllustrative quotes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"9\" valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eWomen\u0026rsquo;s experiences of IOL and childbirth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eBlood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026ldquo;Only was worrying about BP\u0026rdquo; (Interview 3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eChildbirth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026ldquo;As soon as I got there, I had to push hard. Doctor was asking me to push, I pushed hard and delivered normally.\u0026rdquo; (Interview 31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eEnvironment\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026ldquo;I was seeing the ladies so I was more scared. (Interview 11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eFamily\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026ldquo;My family members are happy. Specially my husband is very happy.\u0026rdquo; (Interview 46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eIOL process\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026ldquo;They had given the first pill. It didn\u0026apos;t bring the pain. Given a second pill, there was a backache. After the third pill, I had more pain.\u0026rdquo; (Interview 37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eKnowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026ldquo;I don\u0026apos;t have knowledge about it\u0026hellip; but I heard from my friend, \u0026quot;I went to the hospital and I delivered normally within half an hour\u0026rdquo;. If she deliver normally then why not me?\u0026rdquo; (Interview 45)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003ePain or \u0026apos;traas\u0026apos;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026ldquo;What a pain\u0026hellip; the pain was so much\u0026hellip; I was feeling it\u0026apos;s coming every 2-3 minutes.. such a pain was there... a lot of pain, lot of pain.\u0026rdquo; (Interview 46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eStaff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026ldquo;And then [\u0026hellip;] madam was there. She helped me a lot.\u0026rdquo; (Interview 46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eThoughts and feelings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026ldquo;Nervousness is there na\u0026hellip; How will it happen, what will happen? (Interview 18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eImportant areas identified by women\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eBaby\u0026apos;s future\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026ldquo;Yes... like I became a mother... how will be my baby? What will he do in future?\u0026rdquo; (Interview 37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eBaby\u0026apos;s gender\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026ldquo;There was no tension, whether it\u0026apos;s a baby girl or male.\u0026rdquo; (Interview 3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eMode of birth\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026ldquo;Caesar don\u0026apos;t have trouble early but afterwards there is a lot of trouble\u0026hellip; normal delivery is good.\u0026rdquo; (Interview 8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eTimely birth and discharge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026ldquo;Now, waiting to go home. When it will discharge (smiled).\u0026rdquo; (Interview 10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eOwn health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026ldquo;My BP should not raise.\u0026rdquo; (Interview 10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eNo pain or \u0026apos;traas\u0026apos;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026ldquo;I was feeling that there should not be much pain.\u0026rdquo; (Interview 10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eSafety and health of baby\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026ldquo;My baby should be safe\u0026hellip; I would have done anything for that.\u0026rdquo; (Interview 16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cu\u003eWomen\u0026rsquo;s experiences of IOL and childbirth\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eWomen discussed nine focal areas of the IOL experience. They acknowledged that elevated \u003cstrong\u003eblood pressure\u003c/strong\u003e was the primary reason for induction and recognised its potential risk to their baby\u0026apos;s health. Anxiety, perceived as a key contributor to hypertension, raised concerns among women, with some fearing the possibility of a caesarean section if blood pressure remained high. \u0026nbsp;\u003cstrong\u003eEnvironmental\u003c/strong\u003e factors, including witnessing other women\u0026apos;s struggles during \u003cstrong\u003echildbirth\u003c/strong\u003e, negatively influenced some participants. \u003cstrong\u003eFear and anxiety\u003c/strong\u003e were prevalent emotions during the IOL process, particularly among primiparous women. \u003cstrong\u003eFamily\u003c/strong\u003e advice and comparisons with relatives\u0026apos; childbirth experiences were common topics of discussion. Limited prior \u003cstrong\u003eknowledge\u003c/strong\u003e about IOL methods was noted, and several women were under the impression that the use of \u0026quot;saline\u0026quot; (oxytocin) would result in faster delivery. Despite apprehensions, women generally appreciated the necessity of induction for maternal and foetal safety. Some struggled to distinguish between \u003cstrong\u003eIOL\u003c/strong\u003e and the overall childbirth experience. Pain expectations varied, and contractions were often described as \u0026quot;terrible,\u0026quot; after misoprostol was commenced. Positive relationships with \u003cstrong\u003estaff\u003c/strong\u003e, feeling supported and well-cared-for, and respectful interactions with healthcare providers were highlighted. Women \u003cstrong\u003ethought/felt\u003c/strong\u003e participation in the MOLI trial contributed to better quality care.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eImportant areas identified by women\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eWomen identified seven key priorities during discussions. They often detailed plans for the \u003cstrong\u003ebaby\u0026apos;s future\u003c/strong\u003e, emphasising the importance of the baby remaining well, with considerations for feeding, care, and education. \u003cstrong\u003eGender\u003c/strong\u003e preferences were mentioned by around half of the women, with few expressing disappointment, often deferring to male family members\u0026apos; opinions. Despite a preference for vaginal delivery, women prioritised the safe birth of their \u003cstrong\u003ebaby\u003c/strong\u003e over the \u003cstrong\u003emode of birth\u003c/strong\u003e, with positive sentiments about vaginal birth and some expressing relief after the fact. \u003cstrong\u003eTimely birth and discharge\u003c/strong\u003e were crucial for some, while concerns about \u003cstrong\u003epain and \u0026quot;traas\u0026quot;\u003c/strong\u003e during labour were common priorities. \u003cstrong\u003eOwn health\u003c/strong\u003e issues such as maintenance of normal blood pressure and discussions about family planning operations were also highlighted. Overall, the \u003cstrong\u003esafety and health of the baby\u003c/strong\u003e emerged as the foremost priority for almost all women, often evoking strong emotional responses.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePaGES Index\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e2755 statements were made by the 519 women who completed the postnatal PaGES Index form, of which 1966 were allocated spending points (table 3). \u0026nbsp;All postnatal codes were organised into subthemes and seven overarching themes. \u0026nbsp;Overarching themes identified were \u0026ldquo;family\u0026rdquo;, \u0026ldquo;looking to the future\u0026rdquo;, \u0026ldquo;mother\u0026rsquo;s emotional wellbeing\u0026rdquo;, \u0026ldquo;mother\u0026rsquo;s physical health\u0026rdquo;, \u0026ldquo;perspective on the baby\u0026rdquo;, \u0026ldquo;perspective on the birth\u0026rdquo; and \u0026ldquo;miscellaneous\u0026rdquo;. \u0026nbsp; \u0026ldquo;Satisfaction with normal vaginal delivery\u0026rdquo; (n=183) and \u0026ldquo;baby is healthy\u0026rdquo; (n=209) were the most frequently stated postnatal codes.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 3.\u003c/strong\u003e\u0026nbsp; \u003cstrong\u003ePostnatal PaGES codes.\u003c/strong\u003e\u0026nbsp; The top 10 most frequently cited postnatal PaGES codes with number and percentage of participants citing the code.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"617\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 328px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCode\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 289px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of codes allocated beans (% of participants)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 328px;\"\u003e\n \u003cp\u003eBaby \u0026ndash; healthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 289px;\"\u003e\n \u003cp\u003e212 (40.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 328px;\"\u003e\n \u003cp\u003eMode of Delivery \u0026ndash; satisfaction with Normal Vaginal Delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 289px;\"\u003e\n \u003cp\u003e179 (34.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 328px;\"\u003e\n \u003cp\u003eFamily \u0026ndash; happy/good\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 289px;\"\u003e\n \u003cp\u003e140 (26.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 328px;\"\u003e\n \u003cp\u003eHappy \u0026ndash; to have baby\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 289px;\"\u003e\n \u003cp\u003e145 (27.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 328px;\"\u003e\n \u003cp\u003ePostnatal \u0026ndash; discharge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 289px;\"\u003e\n \u003cp\u003e73 (14.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 328px;\"\u003e\n \u003cp\u003ePostnatal \u0026ndash; care for baby\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 289px;\"\u003e\n \u003cp\u003e124 (23.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 328px;\"\u003e\n \u003cp\u003eEverything was good \u0026ndash; staff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 289px;\"\u003e\n \u003cp\u003e87 (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 328px;\"\u003e\n \u003cp\u003ePain \u0026ndash; general\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 289px;\"\u003e\n \u003cp\u003e50 (9.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 328px;\"\u003e\n \u003cp\u003eGender \u0026ndash; important\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 289px;\"\u003e\n \u003cp\u003e88 (16.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 328px;\"\u003e\n \u003cp\u003eLong-term future \u0026ndash; baby\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 289px;\"\u003e\n \u003cp\u003e77 (14.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 328px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal codes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 289px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1966 (71.4%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;The importance and satisfaction scores for each theme are summarised in table 4. \u0026nbsp;Statements within the \u0026ldquo;perspective on the birth\u0026rdquo; overarching theme were allocated the highest proportion of beans followed by statements relating to \u0026ldquo;perspective on the baby\u0026rdquo;. \u0026nbsp; \u0026ldquo;Mother\u0026rsquo;s physical health\u0026rdquo; statements were allocated the fewest spending points (beans). \u0026nbsp;Mean postnatal satisfaction levels ranged from 5.19 for \u0026ldquo;mother\u0026rsquo;s physical health\u0026rdquo; to 9.03 for \u0026ldquo;family\u0026rdquo; (figure 4).\u003cbr\u003e\u003cbr\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e\u0026nbsp; \u003cstrong\u003eSummary of PaGES theme results.\u003c/strong\u003e\u0026nbsp; Number of women citing each theme, mean postnatal spending point (bean) scores* for all participants who cited each overarching theme with standard deviations (s.d.) and mean satisfaction scores for each of the overarching themes displayed in order of importance. \u0026nbsp;*If a woman cited an overarching theme more than once her scores have been averaged to generate a mean bean score with a numerator where n = number of women.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"646\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber (and %) of women citing the theme\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eImportance \u0026ndash; mean number of beans allocated (+/- SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSatisfaction \u0026ndash; mean score (+/- SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003ePerspective on birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e431 (82.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e8.3 (+/- 5.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e7.06 (+/- 2.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003ePerspective on baby\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e358 (68.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e7.17 (+/- 4.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e8.68 (+/- 2.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eMother\u0026apos;s emotional wellbeing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e210 (40.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e5.08 (+/- 3.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e7.72 (+/- 2.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eLooking to the future\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e396 (76.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e4.96 (+/- 4.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e7.25 (+/- 2.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eFamily\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e180 (34.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e4.05 (+/- 3.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e9.03 (+/- 1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eMother\u0026apos;s physical health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e84 (16.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e1.89 (+/- 2.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e5.19 (+/- 2.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;In the open-ended PaGES assessment, some women mentioned delivery time, pain and anxiety, concepts that were directly questioned in the Likert questionnaires. \u0026nbsp;This allows direct comparison between the methodologies (table 5). \u0026nbsp;The mean satisfaction scores for the 3 questions were very similar in the two instruments, although only 13-24% of women identified the concepts of time to delivery, pain and anxiety as important in the PaGES questionnaire.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Comparison of PaGES Index importance and satisfaction scores with Likert responses.\u0026nbsp;\u003c/strong\u003eThe number of women citing each of these key concepts in the postnatal PaGES forms and the mean number of spending points (beans) allocated to their comments (note that concepts such as pain may be cited in various codes and if so the values have been combined). Mean satisfaction scores for the codes or group of codes denoting each concept are provided and these can be directly compared to the mean Likert scores for these areas.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSatisfaction Questions (Likert scale)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 366px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePaGES Index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSatisfaction Score \u0026ndash; Mean Likert score (+/- SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber (and %) of women citing each concept\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eImportance \u0026ndash; mean number of beans allocated (+/- SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConcept satisfaction (in those who cited it) \u0026ndash; mean score (+/- SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eAcceptability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 366px;\"\u003e\n \u003cp\u003eNo single code or group of codes represent this\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e7.44 (+/- 2.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eDelivery time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003en=68 (13.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e3.13 (+/- 3.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e6.66 (+/- 2.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e6.11 (+/- 2.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003ePain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003en=124 (23.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e2.32 (+/- 3.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e4.82 (+/- 2.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e3.63 (+/- 2.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eAnxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003en=70 (13.5 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e1.81 (+/- 2.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e4.44 (+/- 2.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e4.58 (+/- 2.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eIntegration analyses\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA \u0026lsquo;contiguous approach\u0026rsquo; to data integration was taken for this study (18). \u0026nbsp;Integration analyses were based on the postnatal data of 20 women who completed all three tools. \u0026nbsp;Table 6 demonstrates three participants\u0026rsquo; responses (see Appendix for full table). \u0026nbsp;No association was found between mean Likert scores and overall postnatal PaGES scores, however common themes emerged from the PaGES Index, Likert and qMOLI data (figure 5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWomen who reported \u0026lsquo;high\u0026rsquo; or \u0026lsquo;extreme\u0026rsquo; Likert pain scores almost always discussed experiencing severe labour pain in their qMOLI interview. \u0026nbsp; These women also generally gave a PaGES Index statement relating to pain with a low satisfaction score. \u0026nbsp;For example, participant 1-1190 gave a Likert pain score of 4 (high) and reported that she \u0026ldquo;\u003cem\u003ehad a lot of pain in the abdomen\u0026rdquo;.\u003c/em\u003e\u0026nbsp; She also provided a statement relating to pain on her postnatal PaGES form which had a low satisfaction score of 3. \u0026nbsp;However, there were exceptions such as participant 1-1189 who also gave a Likert pain score of 4 but in her qMOLI interview said \u003cem\u003e\u0026ldquo;I had problems with BP, otherwise I did not have much trouble with labour pains\u0026rdquo;\u003c/em\u003e. \u0026nbsp;This participant discussed delivery time in her qMOLI interview and PaGES form but did not provide a score on Likert- response form. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWomen did not express opinions on either augmentation method unless prompted in qMOLI interviews and PaGES statements, but Likert augmentation scores demonstrated acceptable (49.8%) or very acceptable (28.7%) induction. \u0026nbsp; Most women mentioned mode of birth in PaGES and qMOLI and every woman who expressed dissatisfaction with caesarean section on the postnatal PaGES form also discussed this in their qMOLI interview. \u0026nbsp; For example, participant 2-0102 said \u003cem\u003e\u0026ldquo;In Caesar there is a problem\u0026rdquo;\u0026nbsp;\u003c/em\u003eand made a PaGES statement that coded for\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u0026ldquo;MOB - dissatisfaction with CS\u0026rdquo;\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The PaGES index scores reveal how predetermined Likert scale questions can present false impressions. \u0026nbsp;For two participants (IDs 1-1189 and 1-1190), the Likert scale responses suggest that the participant was unhappy with the birth process. This was based on questions related to time in labour, pain and anxiety. The PaGES Index however reveals that the women\u0026rsquo;s main priorities were not length of time in labour and anxiety, but their baby\u0026rsquo;s safety, the desire to have a vaginal birth and the baby\u0026rsquo;s gender, none of which were asked about in the Likert questions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6. Integration of results.\u003c/strong\u003e\u0026nbsp; Sample Likert, PaGES and qMOLI responses from three of the 20 randomised participants who completed all three tools colour coded with negative (red), neutral (yellow) and positive (green) scores. \u0026nbsp;Overall PaGES scores were calculated by totalling the multiplied postnatal bean and satisfaction scores from each participant\u0026rsquo;s postnatal statements. \u0026nbsp;Scores for all 20 women are shown in Appendix 3.\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003eFigure 5 summarises the key findings of the postnatal PaGES Index, qMOLI interviews and Likert questionnaires, and highlights overlapping themes between participants\u0026rsquo; responses to the three tools. \u0026nbsp; Areas of convergence between the PaGES statements and qMOLI quotes included mentions of staff, mode of birth and the baby\u0026rsquo;s health and wellbeing. \u0026nbsp;Acceptability of delivery time and anxiety were assessed through the Likert questionnaire and were also discussed in qMOLI interviews. \u0026nbsp;Pain was a common theme across all three tools.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this clinical trial on labour induction in India, satisfaction was assessed using three methods and the data compared. Analysis of standard, closed Likert questions revealed that women were generally satisfied with the IOL process with most women reporting the process of augmentation was acceptable (79%), as well as the time it took to deliver (58%). Many, however, stated high levels of pain and anxiety (59% and 45%). Pain, anxiety and fear were also themes from the 20 semi-structured postnatal interviews, but women revealed that the most important concept for them was the safety and health of the baby, and that this took priority over induction method, mode of birth and mother\u0026rsquo;s own health. Other key areas from the interviews were blood pressure, birth experience, pain and the care given by staff. However, following childbirth, women were happy and pleased to have a baby. Like the interviews, the PaGES results found that having a healthy baby was the main priority for women before and after birth. In addition, women highlighted mode of birth as a priority and expressed a clear preference for vaginal birth. \u0026ldquo;Perspective on the birth\u0026rdquo; comments were allocated the highest proportion of beans whilst \u0026ldquo;mother\u0026rsquo;s physical health\u0026rdquo; statements received the fewest beans. Women expressed lowest satisfaction with \u0026ldquo;mother\u0026rsquo;s physical health\u0026rdquo; and highest satisfaction with \u0026ldquo;family\u0026rdquo;. Overall satisfaction in the MOLI study was high, however the PaGES index revealed that it was lower in the specific areas that the women raised as important. The PaGES satisfaction scores and Likert scores yielded similar results for delivery time, pain and anxiety, however of these, only pain was raised as one of the most important issues for women in the postnatal PaGES form.\u003c/p\u003e \u003cp\u003eA major strength of this study was the large study population and high response rate, which increased the validity of findings and allowed for meaningful comparison between the methodologies. However, the small number of interviews means that direct comparisons between the three methodologies was limited to the 20 women in the qualitative study. It was possible however to directly compare the Likert and PaGES results and to compare overall conclusions on satisfaction from each method.\u003c/p\u003e \u003cp\u003eEach of the three methods has strengths and weaknesses. Likert scale questionnaires are quick, efficient and easy to understand measures of satisfaction that can easily be incorporated into large studies (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). As quantitative results are collected on all women within the study, interpretation and statistical analysis are rapid in order to draw conclusions and identify trends. However, the closed questions are predetermined by the research team, and they do not, as seen clearly in this study, necessarily reflect the priorities of the participants. The research team\u0026rsquo;s choice of questions (on pain, anxiety, length of time in labour, and the acceptability of the augmentation method) reflect their perceptions of what might vary between the two arms of the study, not women\u0026rsquo;s overall satisfaction. To the casual reader it therefore gives a distorted view of women\u0026rsquo;s satisfaction. Furthermore, Likert scales have their own methodological challenges including central tendency bias, as participants often avoid using extreme response categories. This was observed in the present study. Response bias may also occur when patients do not feel comfortable expressing their honest response or are reluctant to express negative views to the questioner (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). As only four Likert scale questions were included, we were only able to ascertain limited information regarding patient satisfaction. Another limitation was the lack of synonymity between Likert scales used. Two questions asked participants to rate the acceptability of their augmentation and delivery time on a scale ranging from very acceptable to very unacceptable, whilst the other two asked patients to rate their pain and anxiety on a scale of none to extreme. Thus, scores could not be assimilated to generate an overall score. Finally, we used a 5-point Likert scale. However, a 4-point scale may have been more effective as this would eliminate the possibility of selecting a neutral or moderate response (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eQualitative interviews are a standard method to collect in-depth data on the experiences of study participants. However, the time-consuming nature of both interviews and analysis limits the sample size to when \u0026lsquo;data saturation\u0026rsquo; has been reached \u0026ndash; that is when no new information is being revealed in interviews. This allows the research team to understand the breadth of issues raised by participants, but not their frequency. It is difficult therefore to use the results to generalise about the satisfaction of the participants \u0026ndash; only to describe the range of their experiences. This is in direct contrast to the Likert and PaGES results where the opinion of every participant was reported and frequency could be ascertained. There are additional limitations of the qualitative methodology, mainly centred around transcription and translation errors, though the team attempted to mitigate potential bias from inaccuracies through group discussion.\u003c/p\u003e \u003cp\u003eA major strength of using the PaGES Index in the present study was that all women could be sampled, which allowed for inter-group statistical comparison. The qualitative element of the PaGES Index allowed women to freely give their opinions, whilst the quantitative component facilitated statistical analysis. However, there remained some data complexities to deal with such as bean counts not adding up to 20 and duplicated statements, both of which resulted in minor inaccuracies. Furthermore, there was ambiguity in the coding of certain statements, for example where two potential codes would have been appropriate for one statement. In these instances, a code was allocated based on group consensus, but we found that both codes usually amalgamated into the same, higher level overarching theme regardless. Overall, the PaGES Index was an easier data collection method to incorporate into the trial than interviews, which had to be scheduled separately. It also provides a more detailed insight into women\u0026rsquo;s experiences and satisfaction than a purely quantitative method such as Likert questionnaires. Furthermore, though the PaGES Index was used in a birth setting in this study, this PROM could be adapted to other trials and study populations.\u003c/p\u003e \u003cp\u003ePost data-collection processes, including translating, data cleaning, coding and recoding, are very time consuming and complex. To further simplify the analysis, future studies might consider only implementing the PaGES Index post- intervention. Alternatively, researchers could limit the number of statements made by each participant to 5, which would still be sufficient to capture the participants\u0026rsquo; views. It may also be more practical for participants to select codes from an extensive predetermined list once an initial PaGES dataset for a clinical situation and setting has been obtained and coded.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eComparison with other studies\u003c/h2\u003e \u003cp\u003eAs the PaGES Index was pioneered in this study, there have been no previous published comparisons with other methodologies. Furthermore, virtually all previous qualitative research on birth outcomes, whether induced or not, comes from high income settings. It may seem surprising therefore that our satisfaction findings are consistent with much of the existing quantitative research of women\u0026rsquo;s experience of IOL. Generally, studies assessing satisfaction through Likert scale questions report overall positive birth experiences. A high-income setting study assessing maternal satisfaction with induction using a 10-point scale found that women in both the treatment and placebo groups expressed overall satisfaction following induction and birth (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Qualitative interviews with women however reveal many negative experiences, often due to receiving insufficient information and issues surrounding decision-making, support and environment (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Dissatisfaction with IOL is associated with a lack of knowledge (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), and women are often \u0026ldquo;surprised\u0026rdquo; to find they need to be induced and experience stress whilst awaiting active labour (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Researchers therefore conclude that women should be kept updated at each step of their induction process and healthcare professionals should ensure women are aware about the potential need for additional interventions and pain management (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Fear of childbirth before labour is a risk factor for a negative experience of IOL. Many women reported high levels of pain following induction, a finding that is echoed by other qualitative studies (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). However, no systematic reviews have synthesised evidence relating to women's experiences of pain with various induction methods. Women describing their experiences of IOL often mentioned care from staff, demonstrating that women undergoing IOL can have a positive birth experience if well supported (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eImplications\u003c/h2\u003e \u003cp\u003eConsiderable understanding of the priorities, experience and satisfaction of women in the MOLI study has been gained as a result of the triangulated analysis of the PaGES Index, Likert questionnaire and qMOLI interviews results. Each of the three methods has its own strength and a role to play in assessing birth satisfaction. Likert scales provide rapid data that is easy to assess and analyse, and can be useful if research funds are limited. However, the results need to be viewed with caution as they only reflect the priorities of the research team and do not explore the range or relative importance of participants\u0026rsquo; views. Qualitative data generated through semi-structured interviews is time consuming to collect and analyse, and can only tell of the breadth of experience, not their relative frequency. They cannot therefore be used to compare two randomised groups or to describe the frequency of the experiences. They do, however, effectively explore the depth of the participants\u0026rsquo; experiences and help to explain the quantitative data. They are best used alongside quantitative analyses. The new PaGES Index seeks to provide the \u0026lsquo;best of both worlds\u0026rsquo; with an initial rapid qualitative assessment using open questions, followed by a quantification of both the relative importance and satisfaction of each item generated.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe PaGES Index, Likert questionnaires and semi-structured interviews provide varied data which are difficult to compare directly. The Likert questions only asked about four specific areas, but sample all randomised participants. Conversely, the interviews asked open questions about a range of topics but to a limited sample of women. The PaGES index is a hybrid measure which asks brief open questions about priorities to all women, then allows women to quantify their relative importance and satisfaction with each. This allows highly granular data to be collected and quantified from all participants. This study shows that the PaGES results align with both Likert and interview results. We therefore conclude that the PaGES Index is a feasible PROM for collecting detailed qualitative and quantitative insights. Future research should further validate the PaGES Index in other trials and study populations, including among groups receiving care for other health conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cu\u003eEthics approval and consent to participate\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThis trial is sponsored by the University of Liverpool (Brownlow Hill, Liverpool L69 7ZX, UK; UoL001374) which oversees the study quality and has final responsibility for the study conduct. \u0026nbsp;The study was approved by the Institutional Ethics Committees at Government Medical College Nagpur (1724 EC/Pharmac/GMC/NGP), Spandan Heart Institute and Research Center (MOLI Study), the Mahatma Gandhi Institute of Medical Sciences (MGIMS/IEC/OBGY/96/2020) and the University of Liverpool (UoL001374). \u0026nbsp;The study is insured by the sponsor (for harm arising from protocol design) and by the recruiting sites (for clinical negligence). \u0026nbsp;The MOLI study is registered with ClinicalTrials.gov (NCT03749902, Registration date: 21\u003csup\u003est\u003c/sup\u003e Nov 2018) and Clinical Trial Registry, India (CTRI/2019/04/018827). \u0026nbsp;All women enrolled in the trial provided informed written consent.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eConsent for publication\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAvailability of data and materials\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eUntil full results are published, only the study investigators will have access to the data files. \u0026nbsp;Subsequently, the full database, including the PaGES Index data, will be made available to other researchers upon request. \u0026nbsp;Efforts will also be made to use open access databases to ensure our data is widely accessible with minimal restrictions, adhering to MRC and Wellcome Trust guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eCompeting interests\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eFunding\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eFunding was provided by the Department of Health and Social Care (DHSC), the Foreign, Commonwealth \u0026amp; Development Office (FCDO), the Medical Research Council (MRC) and Wellcome through the Joint Global Health Trials scheme (MR/R006/180/1) and included external peer review. \u0026nbsp;The funder attended Trial Steering Committee meetings, but otherwise played no part in the conduct of the research or writing the paper.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAuthors contributions\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eAW led the grant application, chaired the trial management group and is the guarantor of the MOLI study. \u0026nbsp;SM was the lead investigator in India. \u0026nbsp;AS, KL, BF and BW were also involved in the planning and preparation of the MOLI RCT. \u0026nbsp;Data collection was overseen by SM, JD and KL. \u0026nbsp;The PaGES index data was coded by RH and AP. \u0026nbsp;The data analyses presented in this paper were carried out by AP with input from BF. \u0026nbsp;All authors had full access to all the data in the study and contributed to the checking of the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAcknowledgements\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the MRC, Wellcome Trust, NIHR and UK government for providing funding for the study through the Joint Global Health Trials scheme. \u0026nbsp;We also thank Moushmi Tadas, Seema Parvekar, Poonam Varma Shivkumar and the research teams who ran the MOLI study in India and collected the data that made this secondary analysis possible.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eLavela S, Gallan A. Evaluation and measurement of patient experience. Patient Exp J. 2014;1(1):28-36.\u003c/li\u003e\n \u003cli\u003eWorld Health Organization UNPF, UNICEF. Managing Complications in Pregnancy and Childbirth: A guide for midwives and doctors 2017 [cited 2020 24 May]. Available from: https://apps.who.int/iris/bitstream/handle/10665/255760/9789241565493-eng.pdf;jsessionid=0A0BF303B9D76469B230A0ACB8CF8440?sequence=1.\u003c/li\u003e\n \u003cli\u003eNagpal J, Sachdeva A, Sengupta Dhar R, Bhargava VL, Bhartia A. Widespread non-adherence to evidence-based maternity care guidelines: a population-based cluster randomised household survey. British Journal of Obstetrics and Gynaecology. 2015;122(2):238-47.\u003c/li\u003e\n \u003cli\u003eVogel JP, Souza JP, G\u0026uuml;lmezoglu AM. Patterns and Outcomes of Induction of Labour in Africa and Asia: a secondary analysis of the WHO Global Survey on Maternal and Neonatal Health. PloS one. 2013;8(6):65612.\u003c/li\u003e\n \u003cli\u003eHenderson J, Redshaw M. Women\u0026apos;s experience of induction of labor: a mixed methods study. Acta obstetricia et gynecologica Scandinavica. 2013;92(10):1159-67.\u003c/li\u003e\n \u003cli\u003eGatward H, Simpson M, Woodhart L, Stainton MC. Women\u0026apos;s experiences of being induced for post-date pregnancy. Women and birth : journal of the Australian College of Midwives. 2010;23(1):3-9.\u003c/li\u003e\n \u003cli\u003eAlfirevic Z, Keeney E, Dowswell T, Welton NJ, Medley N, Dias S, et al. Which method is best for the induction of labour? A systematic review, network meta-analysis and cost-effectiveness analysis. Health Technol Assess. 2016;20(65):1-584.\u003c/li\u003e\n \u003cli\u003eNilv\u0026eacute;r H, Begley C, Berg M. Measuring women\u0026rsquo;s childbirth experiences: a systematic review for identification and analysis of validated instruments. BMC pregnancy and childbirth. 2017;17(1):203.\u003c/li\u003e\n \u003cli\u003eWijma K, Wijma B, Zar M. Psychometric aspects of the W-DEQ; a new questionnaire for the measurement of fear of childbirth. Journal of psychosomatic obstetrics and gynaecology. 1998;19(2):84-97.\u003c/li\u003e\n \u003cli\u003eSawyer 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 and childbirth. 2013;13(1):108.\u003c/li\u003e\n \u003cli\u003eRedshaw M, Martin CR. Validation of a perceptions of care adjective checklist. Journal of evaluation in clinical practice. 2009;15(2):281-8.\u003c/li\u003e\n \u003cli\u003eHarvey S, Rach D, Stainton MC, Jarrell J, Brant R. Evaluation of satisfaction with midwifery care. Midwifery. 2002;18(4):260-7.\u003c/li\u003e\n \u003cli\u003eMundle S, Lightly K, Durocher J, Bracken H, Tadas M, Parvekar S, et al. Oral misoprostol alone, compared with oral misoprostol followed by oxytocin, in women induced for hypertension of pregnancy: A multicentre randomised trial. BJOG: An International Journal of Obstetrics \u0026amp; Gynaecology. 2024;n/a(n/a).\u003c/li\u003e\n \u003cli\u003eRuta DA, Garratt AM, Leng M, Russell IT, MacDonald LM. A new approach to the measurement of quality of life. The Patient-Generated Index. Medical care. 1994;32(11):1109-26.\u003c/li\u003e\n \u003cli\u003eSymon A, Lightly K, Howard R, Mundle S, Faragher B, Hanley M, et al. Introducing the participant-generated experience and satisfaction (PaGES) index: a novel, longitudinal mixed-methods evaluation tool. BMC Med Res Methodol. 2023;23(1):214.\u003c/li\u003e\n \u003cli\u003eBracken H, Lightly K, Mundle S, Kerr R, Faragher B, Easterling T, et al. Oral Misoprostol alone versus oral misoprostol followed by oxytocin for labour induction in women with hypertension in pregnancy (MOLI): protocol for a randomised controlled trial. BMC pregnancy and childbirth. 2021;21(1):537.\u003c/li\u003e\n \u003cli\u003eGale N, Heath G, Cameron E, Rashid S, Redwood S. Using the framework method for the analysis of qualitative data in multi-disciplinary health research. BMC Medical Research Methodology. 2013;13(1):117.\u003c/li\u003e\n \u003cli\u003eFetters MD, Curry LA, Creswell JW. Achieving integration in mixed methods designs-principles and practices. Health Serv Res. 2013;48(6 Pt 2):2134-56.\u003c/li\u003e\n \u003cli\u003eJebb AT, Ng V, Tay L. A Review of Key Likert Scale Development Advances: 1995\u0026ndash;2019. Frontiers in Psychology. 2021;12.\u003c/li\u003e\n \u003cli\u003eWestland JC. Information loss and bias in likert survey responses. PloS one. 2022;17(7):e0271949.\u003c/li\u003e\n \u003cli\u003eGarland R. The Mid-Point on a Rating Scale: Is it Desirable? Marketing Bulletin. 1991;2:66-70.\u003c/li\u003e\n \u003cli\u003eBollapragada S, MacKenzie F, Norrie J, Eddama O, Petrou S, Reid M, et al. Randomised placebo-controlled trial of outpatient (at home) cervical ripening with isosorbide mononitrate (IMN) prior to induction of labour \u0026ndash; clinical trial with analyses of efficacy and acceptability. The IMOP Study. BJOG: An International Journal of Obstetrics \u0026amp; Gynaecology. 2009;116(9):1185-95.\u003c/li\u003e\n \u003cli\u003eCoates R, Cupples G, Scamell A, McCourt C. Women\u0026apos;s experiences of induction of labour: Qualitative systematic review and thematic synthesis. Midwifery. 2019;69:17-28.\u003c/li\u003e\n \u003cli\u003eDupont C, Blanc-Petitjean P, Cortet M, Gaucher L, Salom\u0026eacute; M, Carbonne B, et al. Dissatisfaction of women with induction of labour according to parity: Results of a population-based cohort study. Midwifery. 2020;84:102663.\u003c/li\u003e\n \u003cli\u003eLundh C, \u0026Oslash;vrum AK, Dahl B. Women\u0026apos;s experiences with unexpected induction of labor: A qualitative study. Eur J Midwifery. 2023;7:7.\u003c/li\u003e\n \u003cli\u003eRCM. Midwifery care for Induction of Labour. 2019.\u003c/li\u003e\n \u003cli\u003eJay A, Thomas H, Brooks F. In labor or in limbo? The experiences of women undergoing induction of labor in hospital: Findings of a qualitative study. Birth (Berkeley, Calif). 2018;45(1):64-70.\u003c/li\u003e\n \u003cli\u003eLima B, Ribeiro MMA, Martins Rose Costa E, Concei\u0026ccedil;\u0026atilde;o de Almeida Ramos R, Francisco MTR, Val\u0026eacute;rio Machado de Lima D. Feelings amongst high-risk pregnant women during induction of labor: a descriptive study. Online Braz J Nurs. 2016;15:254-64.\u003c/li\u003e\n \u003cli\u003eFord E, Ayers S, Wright DB. Measurement of maternal perceptions of Support and Control in Birth (SCIB). Journal of Women\u0026apos;s Health. 2009;18(2):245-52.\u003c/li\u003e\n \u003cli\u003eDencker A, Taft C, Bergqvist L, Lilja H, Berg M. Childbirth experience questionnaire (CEQ): development and evaluation of a multidimensional instrument. BMC pregnancy and childbirth. 2010;10(1):81-8.\u003c/li\u003e\n \u003cli\u003eVan der Kooy J, Valentine NB, Birnie E, Vujkovic M, de Graaf JP, Denktaş S, et al. Validity of a questionnaire measuring the world health organization concept of health system responsiveness with respect to perinatal services in the dutch obstetric care system. BMC Health Services Research. 2014;14(1):622.\u003c/li\u003e\n \u003cli\u003eSjetne IS, Iversen HH, Kj\u0026oslash;llesdal JG. A questionnaire to measure women\u0026rsquo;s experiences with pregnancy, birth and postnatal care: instrument development and assessment following a national survey in Norway. BMC pregnancy and childbirth. 2015;15(1):182.\u003c/li\u003e\n \u003cli\u003eTruijens SE, Wijnen HA, Pommer AM, Oei SG, Pop VJ. Development of the Childbirth Perception Scale (CPS): perception of delivery and the first postpartum week. Archives of women\u0026apos;s mental health. 2014;17(5):411-21.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Participant, experience, satisfaction, patient-generated, qualitative research, quantitative research, induction of labour, instrument, birth, patient reported experience measure, patient reported outcome measure","lastPublishedDoi":"10.21203/rs.3.rs-5417470/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5417470/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eBackground\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAlthough induction of labour is becoming more common worldwide, there are few studies that assess women’s satisfaction with it. \u0026nbsp;The newly developed Participant Generated Experience and Satisfaction (PaGES) Index collects brief qualitative data and quantifies it, allowing detailed satisfaction data to be collected on large populations. \u0026nbsp;The PaGES data has never previously been compared to other methods of assessing study participants’ satisfaction. \u0026nbsp;We aimed to triangulate PaGES Index, Likert questionnaire and interview data from a large, randomised trial of labour induction to fully understand women’s priorities, experience and satisfaction and to compare the findings of the three instruments.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMethods\u003c/em\u003e\u003cstrong\u003e \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA convergent parallel multi-methods research design was used.\u0026nbsp; Participants in the Misoprostol or Oxytocin for Labour Induction (MOLI) trial (n=520) completed the PaGES Index before and after birth, listing priorities and allocating spending points to demonstrate their relative importance.\u0026nbsp; Postnatally, participants scored their satisfaction with each item.\u0026nbsp; Quantitative data was collected postnatally on the acceptability of augmentation, delivery time, pain and anxiety using a Likert scale.\u0026nbsp; Semi-structured interviews were also conducted, and thematic analysis was carried out using a framework approach.\u0026nbsp; The data from 20 participants who had completed all three outcome measures were integrated and compared.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eResults\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAlthough common themes, such as pain, emerged from participants’ responses to the three instruments, each provided different insights. \u0026nbsp;The Likert responses demonstrated overall satisfaction with the induction process but with high levels of pain and anxiety. \u0026nbsp;Semi-structured interviews highlighted that safety and health of the baby was a key priority. \u0026nbsp;The PaGES Index confirmed that the baby’s wellbeing was most important to women, but women also expressed a strong preference for vaginal delivery. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConclusions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe PaGES Index, Likert questionnaire and semi-structured interview data provide varied but complimentary insights on women’s birth experiences and their satisfaction with their induction process.\u0026nbsp; The outputs of the three methods align, but the PaGES index was unique in capturing both detailed qualitative and quantitative information for all study participants.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTrial registration\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe MOLI study is registered in ClinicalTrials.gov (NCT03749902, Registration date: 21\u003csup\u003est\u003c/sup\u003e Nov 2018) and Clinical Trial Registry, India (CTRI/2019/04/018827)\u003c/p\u003e","manuscriptTitle":"Evaluating women’s experiences and satisfaction with labour induction in India: a comparison of the Participant Generated Experience and Satisfaction (PaGES) Index with standard methods","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-18 16:39:17","doi":"10.21203/rs.3.rs-5417470/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-18T11:10:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-16T09:52:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-16T09:50:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pregnancy and Childbirth","date":"2024-11-08T15:04:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ecbf496c-a276-4c21-a0bc-702c8d62e98a","owner":[],"postedDate":"December 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-02T16:05:39+00:00","versionOfRecord":{"articleIdentity":"rs-5417470","link":"https://doi.org/10.1186/s12884-025-07731-9","journal":{"identity":"bmc-pregnancy-and-childbirth","isVorOnly":false,"title":"BMC Pregnancy and Childbirth"},"publishedOn":"2025-05-28 15:57:29","publishedOnDateReadable":"May 28th, 2025"},"versionCreatedAt":"2024-12-18 16:39:17","video":"","vorDoi":"10.1186/s12884-025-07731-9","vorDoiUrl":"https://doi.org/10.1186/s12884-025-07731-9","workflowStages":[]},"version":"v1","identity":"rs-5417470","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5417470","identity":"rs-5417470","version":["v1"]},"buildId":"zQwnuV7TCBrMSSSToR1PI","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}