Realistic Expectations: Investing in Organizational Capacity Building for Chronic Disease Prevention
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it