Building on a foundation: strategies, processes and outcomes of health promotion in primary health care settings
Bibliographic record
Abstract
Jurisdictions around the world have articulated the need for the development of an integrated health care system with an increased emphasis on primary health care that incorporates the principles/practices of health promotion. Over the past century, the medical model has been the default model of care in many countries, and yet treatment alone is unlikely to have marked effects on health inequities or health status. This article presents and discusses three fundamental dimensions (strategies, processes and outcomes) of health promotion in primary health care (HP in PHC) settings. We argue that the three dimensions are founded on the values and structures of health promotion (Frankish et al., 2006). Our work is based on a comprehensive literature review, validation by key informants and a national survey of Canadian primary health care settings. We suggest that the strategies (types of interventions), processes (client and community centred care), and desired health promotion outcomes (intended or unintended results) need to be better articulated and understood. Identification and discussion of the domains of HP in PHC settings is a crucial first step. It is a step toward the subsequent identification of related indicators and measures of health promotion that can be used for planning, implementation and evaluation of important health promotion initiatives.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".