Disease Prevention and Health Promotion Strategies: The Possible Side Effects of Their Good Intentions
Bibliographic record
Abstract
The public health policies are principally implemented using two main strategies, namely, the population strategy and the high-risk strategy. The purpose of this article is to discuss possible side effects of the good intentions of these two main strategies. The discussions herein are made based on our perspectives and literature study methodology. Main findings portray that the disease prevention and health strategies are applied on a skewed basis, and more so, they are mainly based on medical culture and take little account of human culture. This implies that in order for individuals to comply with the health authorities’ demands, they must give up their own lifestyle coping-strategies that are contradictive to the demands. Hence, the possible side effects of the disease prevention and health promotion strategies’ good intentions; as the strategies have no explicit mandate to change the cultural norms and values. Therefore, we argue that adaptations to make the strategies more inclusive may promote public healthcare in the sense that it can work for everyone’s lifestyle, as individuals can easily take healthy actions in the normal course of their lives.
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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 | 0.033 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".