Realistic Expectations: Investing in Organizational Capacity Building for Chronic Disease Prevention
Bibliographic record
Abstract
PURPOSE: This article presents findings that explore investment in organizational capacity building for chronic disease prevention. Specifically, this analysis examines variation in investment inputs, intervention outputs, and capacity changes to inform expectations of health-promotion capacity-building investment. DESIGN/SETTING: This multiple case study involving both qualitative and quantitative data is based on seven provincial dissemination projects involved in the Canadian Heart Health Initiative. METHODS: Data on investment, number, and type of capacity-building activities and capacity changes come from a questionnaire, key informant interviews, and project report analysis. Quantitative data were analyzed descriptively and for trends, while qualitative data were analyzed thematically. RESULTS: Per capita investments in capacity building ranged from a low of $0.21 in Ontario to $167.41 in Prince Edward Island. Multiple, tailored capacity-building interventions were used in each project. Mostly positive but modest changes were observed in at least five dimensions of capacity in all but one project. CONCLUSION: These findings reveal that capacity building for chronic disease prevention requires a long-term investment and is context specific. Even limited investment can produce interventions that appear to positively influence capacity for chronic disease prevention. The findings also suggest an urgent need to expand surveillance to include indicators of capacity-building investments and interventions to allow policy makers to make more informed decisions about investments in public health.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".