Community based sensitization to address maternal and child health problems in tribal population of India
Bibliographic record
Abstract
Background: India fights with substantial maternal and child health (MCH) concerns, accounting about one quarter of the global burden of maternal and childhood mortality. The current study was tried to assess the impact of community partnerships between medical students, community stakeholders (TBAs and local tribal girls) and general community members on their awareness levels about MCH care and services.Methods: a community-based pilot interventional study was conducted at one of the rural blocks of Maharashtra state of India. Of 120, sixty (50%) first year undergraduate MBBS medical students (intervention group) posted at two months rural healthcare training programme’ participated in preparing MCH related health education material (HEM) in local language. Similarly local tribal girls, TBAs and general community people were trained about MCH and their knowledge levels were assessed.Results: Pre and post training assessment scores of participants (students, TBAs, tribal girls and general people) indicated significant (p<0.05) differences in their knowledge about MCH. The used approach of community collaborations in this study upgraded the knowledge of stakeholders (TBAs, tribal girls) and common tribal people about basic aspects of MCH and associated welfare schemes. The study also reported positive attitudes of all participants about an intervention.Conclusions: Productive and synergistic community partnerships can be created among health care providers, community health workers and other stakeholders to ensure commitment and engagement towards positive health.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".