Association between organizational capacity and involvement in chronic disease prevention programming among Canadian public health organizations
Bibliographic record
Abstract
In the context of the emerging field of public health services and systems research, this study (i) tested a model of the relationships between public health organizational capacity (OC) for chronic disease prevention, its determinants (organizational supports for evaluation, partnership effectiveness) and one possible outcome of OC (involvement in core chronic disease prevention practices) and (ii) examined differences in the nature of these relationships among organizations operating in more and less facilitating external environments. OC was conceptualized as skills and resources/supports for chronic disease prevention programming. Data were from a census of 210 Canadian public health organizations with mandates for chronic disease prevention. The hypothesized relationships were tested using structural equation modeling. Overall, the results supported the model. Organizational supports for evaluation accounted for 33% of the variance in skills. Skills and resources/supports were directly and strongly related to involvement. Organizations operating within facilitating external contexts for chronic disease prevention had more effective partnerships, more resources/supports, stronger skills and greater involvement in core chronic disease prevention practices. Results also suggested that organizations functioning in less facilitating environments may not benefit as expected from partnerships. Empirical testing of this conceptual model helps develop a better understanding of public health OC.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".