Utilisation of maternal and child health handbook in Mongolia: A cross-sectional study
Bibliographic record
Abstract
Objective: This study investigated the use of a Maternal and Child Health (MCH) handbook, and related factors, in Mongolia. Design: Population-based cross-sectional study. Setting: Bulgan Province, Mongolia. Method: MCH handbook use was determined by examining whether participants had read it or recorded their health-related information into it. Multiple logistic regression analysis was performed to reveal factors related to MCH handbook utilisation. Results: Of the 716 participants, 631 (88.1%) read the MCH handbook and 428 (59.8%) recorded their health-related information in it. Mothers with middle or high educational attainment were more likely to have read it than were those with low educational attainment (adjusted odds ratio [AOR] = 2.52, 95% confidence interval [CI] = 1.41–4.50; AOR = 3.19, 95% CI = 1.29–7.93, respectively). Literate women and those who had been taught to use the handbook were more likely to read it (AOR = 3.19, 95% CI = 1.68–6.05; AOR = 2.42, 95% CI = 1.31–4.46, respectively). Mothers with a middle or very high wealth index were more likely to have read it than were those with a very low index. Mothers with middle or high educational attainment were more likely to make records in it than were those with low attainment. Mothers who were taught to use the handbook were more likely to make records in it, while those who had children with chronic diseases were less likely to do so. Conclusion: Women’s literacy levels, educational attainment, economic status and effective explanation of its usage must be considered in order to enhance the handbook’s effectiveness.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".