Rural women's voices revealing perceptions about decisions on where to give birth in Gabon: A qualitative study
Bibliographic record
Abstract
OBJECTIVE: This study aimed to clarify the perceptions of rural women about their decisions on where to give birth in Gabon. METHOD: This study used a qualitative descriptive design using semi-structured interviews. Study participants were women at least 20 years old and had given birth within the past 2 years. The study area was approximately 25-30 km from the capital of Gabon. Data collection was conducted between May and mid-July 2023. The interview guide was based on the Ottawa Decision Support Framework (ODSF) 2020 model. The data obtained were analyzed using content analysis for "perceptions in deciding the place of birth." RESULTS: A total of 18 women participated in the study. Six categories of reasons were identified for women's choice of birth location: (1) childbirth environment with physical safety; (2) childbirth environment with psychological safety; (3) physical accessibility; (4) affordable health facilities; (5) concerns about homebirth risks; and (6) unpleasant aspects of the hospital. Items (1)-(4) were the reasons for actively choosing the hospital as a birth location, whereas items (5) and (6) were the reasons for avoiding a place to give birth. CONCLUSIONS: Women positively perceived and chose facilities that offer physical and mental safety, geographic accessibility, and affordable costs. Conversely, an environment where the safety of the mother and the child is threatened and the lack of respectful maternity care by the medical staff served as deterrents to facility use.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".