Setting standards in the evaluation of community-based health promotion programmes-- a unifying approach
Bibliographic record
Abstract
Community-based health promotion often emphasizes elements of empowerment, participation, multidisciplinary collaboration, capacity building, equity and sustainable development. Such an emphasis may be viewed as being in opposition to equally powerful notions of evidence-based decision making and accountability, and with funders' and government decision-makers' preoccupation with measuring outcomes. These tensions may be fuelled when community practitioners and lay participants feel evaluations are imposed upon them in a manner that fails to appreciate the uniqueness of their community, its programme, and practitioners' skills and experience. This paper attempts to provide an approach that depicts evaluation as being mutually beneficial to both funders/government and practitioners. First, a values stance for health promotion, termed a 'salutogenic' orientation, is proposed as a foundation for the evaluation of community-based health promotion. Secondly, we discuss possible objects of interest, the first component of an evaluation. We then discuss the spirit of the times and its implications for community-based health promotion. Finally, we address the key question of setting standards. A typology of standards is presented. Arbitrary, experiential and utility standards are based on perceived needs and priorities of practitioners, lay participants or professional decision-makers. Historical, scientific and normative standards are driven by empirical, objective data. Propriety and feasibility standards are those wherein the primary concern is for consideration of resources, policies, legislation and administrative factors. The 'model' standards approach is presented as an exemplar of a combined approach that incorporates elements of each of the other standards. We argue that the 'optimal' standard for community-based health promotion depends on the setting and the circumstances. There is no 'magic bullet', 'one-size-fits-all' or 'best' standard. Further, we argue that standards should be set from an inclusive, salutogenic orientation. This approach offers a means of creating a situation in which policy-makers and funders are more supportive of evaluation designs that fit with community realities, and community stakeholders are more capable and consistent in rigorously evaluating community-based health promotion programmes and policies.
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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 | 0.478 | 0.379 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.010 | 0.064 |
| Scholarly communication | 0.046 | 0.029 |
| Open science | 0.007 | 0.028 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".