Bibliographic record
Abstract
This study aims to determine the hospital practices implemented before, during, and after birth in Turkey, and to identify the support to families during this process. Purposive sampling was used in the study to identify the hospitals to be surveyed. Ankara, İstanbul, and İzmir, the provinces with the highest number of hospitals in Turkey, were selected for the collection of data. There were 178 eligible private hospitals in these provinces. The questionnaire form of the Canadian Hospitals Maternity Policies and Practices Survey was used to collect data and the necessary permissions were obtained. There are many restrictions and variations in maternity-related hospital practices in Turkey. 71.4% of the responding private hospitals identified themselves as baby-friendly. 57.1% had a policy facilitating families being together immediately after birth, 39.5% encouraged the presence of the father in the delivery room. 44.9% did not have any policy for assessing women who were going home to potentially violent situations. Eighty-five percent reported that they did not have a written policy or guidelines about procedures regarding labor, birth, or postpartum periods. This study recommended that private hospitals should review their maternal practices and routines in an evidence-based way that helps parents, culturally sensitive standards for mothers should be developed and supervised by related health authorities, and structures should be created to effectively deal with patients’ complaints.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".