Patient Involvement in Safe Delivery: A Qualitative Study
Bibliographic record
Abstract
INTRODUCTION: Patient involvement in safe delivery planning is considered important yet not widely practiced. The present study aimed at identifythe factors that affect patient involvementin safe delivery, as recommended by parturient women. METHODS: This study was part of a qualitative research conducted by content analysis method and purposive sampling in 2013.The data were collected through 63 semi-structured interviews in4 hospitalsand analyzed using thematic content analysis. The participants in this research were women before discharge and after delivery. Findings were analyzed using Colaizzi's method. RESULTS: Four categories of factors that could affect patient involvement in safe delivery emerged from our analysis: patient-related (true and false beliefs, literacy, privacy, respect for patient), illness-related (pain, type of delivery, patient safety incidents), health care professional-relatedand task-related factors (behavior, monitoring &training), health care setting-related (financial aspects, facilities). CONCLUSION: More research is needed to explore the factors affecting the participation of mothers. It is therefore, recommended to: 1) take notice of mother education, their husbands, midwives and specialists; 2) provide pregnant women with insurance coverage from the outset of pregnancy, especially during prenatal period; 3) form a labor pain committee consisting of midwives, obstetricians, and anesthesiologists in order to identify the preferred painless labor methods based on the existing facilities and conditions, 4) carry out research on observing patients' privacy and dignity; 5) pay more attention on the factors affecting cesarean.
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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 | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".