Arabic Language Surveys Measuring Mothers’ Satisfaction During Childbirth: A Review
Bibliographic record
Abstract
PURPOSE: To review Arabic surveys used to measure maternal satisfaction. METHODOLOGY: Peer-reviewed studies published in English and Arabic since 2000 were reviewed across eight databases. Surveys were assessed by: survey construction, reliability, and validity. FINDINGS: The seven studies that met the inclusion criteria were in English and included seven different Arabic surveys. Survey items ranged from eight to 32 and were translated from English (3/7) or were originally written in Arabic (4/7). Six surveys were pilot tested. Dimensions covered by the surveys varied but all measured satisfaction about providers’ interpersonal care. Internal reliability was reported for four surveys and none reported the test-re-test results. Three studies reported content validity, one reported face validity, one reported construct validity, and none reported criterion validity. Participants’ inclusion criteria varied but all studies excluded women with still births or obstetric complications. When surveyed within hospital (3/7), participants were approached within 72 hours after delivery while those surveyed outside the hospital were approached two weeks, seven weeks, or two months after discharge. Overall, the eight-item survey was found short, well tested with good psychometric properties. CONCLUSIONS: The psychometric properties of Arabic surveys were determined in limited settings, were not well reported, and varied. The eight-item survey is a well-tested survey with good psychometric properties. Furthermore, rigorous evaluation of Arabic surveys in different contexts with wider inclusion criteria is required. Our findings will promote further research in this area and will help enhance maternal experience with childbearing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".