Health promotion activities in Ontario Community Health Centres: a descriptive report
Bibliographic record
Abstract
Background Community Health Centres (CHCs) in Ontario, Canada have been delivering health promotion programming as part of their model of comprehensive primary health care to communities for decades. The purpose of this paper is to describe how health promotion programming is delivered within the context of Ontario CHCs. Methods This report used both quantitative and qualitative methods. Electronic medical record data were used to describe the variety of health promotion programming offered, as well as target populations, funding sources, and evaluation. Six focus groups were conducted with 72 health promotion staff and managers across 42 CHCs between February and March of 2023 to provide greater context on how health promotion is delivered. Results In 2021-2022, 2452 programs were reported amongst 69 CHCs, with the top three priorities for programming being social support, food security and education, and physical activity. The most prevalent target populations reported were the general community (24%) and children and youth (15%). Focus group data revealed that priorities are identified through a variety of approaches to ensure programs are relevant, address the needs of their communities, and promote health equity. Programs provided by CHCs leverage community volunteers to support program development and delivery, reduce barriers to participating, and link participants to other programs and services offered by the CHC. Conclusion Health promotion as an upstream approach can relieve pressure on the healthcare system, reduce disease prevalence and health inequities all while being cost-effective. Greater investments in health promotion are needed if we want to support and sustain our healthcare systems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".