Effects of Recruiting Midwives into a Family Physician Program on the Indices of Maternal Health Program in the Rural Areas of Kurdistan
Bibliographic record
Abstract
A family physician program has been implemented in rural areas of the country since the early 2005.Therefore, due to the increase in the density of midwives in this project, it is expected that more services would be provided to pregnant women. This cross-sectional study used the difference-in-differences model and Matchit statistical model to compare the indices of maternal health program before and after the implementation of a family physician program. It compared health centres that had increase in their density of midwives in the course of the study with those that did not. The study sample consisted of 668 mothers of 2-month-old children in 2013. Data were collected using a questionnaire that was administered in structured interviews. In this study, in 2013, 38.8% of the women received preconception care, 66.5% received prenatal care and 41.6% received postpartum care, as defined by the standards. Based on the results of statistical models of difference-in-differences analyses and Matchit, there was no significant change in indices of maternal health program between 2005 and 2013. The results of this study showed that an increase in the density of midwives in a family physician program did not have an impact on the indices of maternal health program; it indicated that the increase in the density of midwives alone was not efficient. In other words, the quality of primary health care is strongly dependent on the use of trained health workers. In addition, manpower planning and management can have an important role in improvement of prenatal care.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".