Designer's Corner - Conceiving Action,Tracking Practice, and Locating Expertise for Health Promotion Research
Bibliographic record
Abstract
Background Health promotion is the process of enabling persons, families, neighbourhoods, communities, sectors, and societies to take action around the development and implementation of health determinants.The goal is to put health determinants in the control of individuals through programming that enhances health promotion action at many levels.The following actions are health promotional: building healthy public policy, reorienting health services, strengthening community action, creating supportive environments, and developing personal skills. A health promotion program that is based on the following principles has a good likelihood of succeeding: comprehensive cross-action programming that contextualizes efforts; participation by all stakeholders in all stages of development, implementation, and evaluation; and capacity building that includes advocacy, enabling, and mediating approaches (Stewart, 1999; Wass, 2000). In order to contribute to the health of Canadians, health promotion programming and research must take into account these actions and principles and the relationship among them. The evaluation of health promotion programming is based on several factors. First, the model selected must facilitate the conceptualization and implementation of both health promotion action, at all levels, and health promotion principles. Second, the practices associated with health promotion must be documented rigorously at all levels of action.Third, effective means of measuring the desired outcome — enhanced control over the determinants of health — must be developed and used.
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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 | 0.073 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.023 | 0.014 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".