5P Health Shield for BOP Citizens: A Self-Care Approach
Bibliographic record
Abstract
Given the appalling health statistics of the Indian sub-continent, a study was conducted to understand how people could be incentivized to lead healthy lives. To this effect, six board categories of incentives were identified. The results of understanding the association between variables indicated that the incentive for behavior change pertaining to self-care had no correlation with the age and gender but education levels played a significant role. Respondents who were graduates and above indicated that professional involvement, community cooperation, information assimilation & dispensing related support could improve their behavior pertaining to self-care. In corollary, individuals who are relatively less educated could be incentivized with therapeutic counselling, monetary incentives and family support. This study also highlighted the importance of women as the torch bearers of preventive healthcare and acknowledged their role in mobilizing the tenets of self-care. The need for an over-arching 5P Health Shield Program/Value Your Health Program is emphasized at a pan-India level. The former program is recommended for the less educated and marginalized while the later for the more educated and fortunate. All healthcare touch points like hospitals, nursing homes, primary care centres, private practices, pharmacies etc operating within the nation, should be mandated to assist in getting every Indian within the fold of either one of these Health Programs. Based on personal health scores/health parameters/risk factors, every Indian should be mapped to a specific health plan for eliciting the necessary guidance/support to prevent or predict the onset of health ailments, or else, protect or preserve oneself from its sinister outcomes. The program also proposes a partnership oriented plan for the chronically ill.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".