Access to health care among rural population
Bibliographic record
Abstract
Background: Good health is very important to human productivity and the “development” process, economic and technological development of the individual as well as for the nation. A healthy community is an asset to nation. The progress of nation is measured by health of its people because healthy people can contribute for the betterment and progress of the nation. Objectives: Toassess the existing status, adequacy and utilization of health services under NRHM in the selected areas in Mayurbhanj & Jagatsinghpur district of the state, Methodology: This is a mixed-method study conducted at Erasama block of Jagatsinghpur District & Baripada Block of Mayurbhanj district in Odisha. Both Primary and secondary data has been used for the study. As a part of qualitative component, n-depth interview and Focussed group discussion were conducted among a subgroup of study participants. Study was conducted from April-2018 to March -2019inAmbiki village and Rangamatia of Erasama and Baripada Blocks respectively. Findings of study: Most of the ASHAs are providing services to a population of more than the specified standard of 1,000. Transportation ofantenatal mothers was found to be a major problem. The total compensation ASHAs receive for each month was comparatively low.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".