Promoting social responsibility for health: health impact assessment and healthy public policy at the community level
Bibliographic record
Abstract
The 1997 Jakarta Declaration on Health Promotion into the 21st Century called for new responses to address the emerging threats to health. The declaration placed a high priority on promoting social responsibility for health, and it identified equity-focused health impact assessment as a high priority for action. This theme was among the foci at the 2000 Fifth Global Conference on Health Promotion held in Mexico. This paper, which is an abbreviation of a technical report prepared for the Mexico conference, advances arguments for focusing on health impact assessment at the local level. Health impact assessment identifies negative health impacts that call for policy responses, and identifies and encourages practices and policies that promote health. Health impact assessment may be highly technical and require sophisticated technology and expertise. But it can also be a simple, highly practical process, accessible to ordinary people, and one that helps a community come to grips with local circumstances that need changing for better health. To illustrate the possibilities, this paper presents a case study, the People Assessing Their Health (PATH) project from Eastern Nova Scotia, Canada. It places ordinary citizens, rather than community elites, at the very heart of local decision-making. Evidence from PATH demonstrates that low technology health impact assessment, done by and for local people, can shift thinking beyond the illness problems of individuals. It can bring into consideration, instead, how programmes and policies support or weaken community health, and illuminate a community's capacity to improve local circumstances for better health. This stands in contrast to evidence that highly technological approaches to community-level health impact assessment can be self-defeating. Further development of simple, people-centred, low technology approaches to health impact assessment at the local level is called for.
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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 | 0.068 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.008 | 0.053 |
| Scholarly communication | 0.025 | 0.023 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.011 | 0.009 |
| 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".