Issues in measuring health promotion capacity in Canada: a multi-province perspective
Bibliographic record
Abstract
Significant international progress has been made researching and addressing the economic and social burden of cardiovascular disease, advanced particularly by international conferences and subsequent declarations, and the Canadian Heart Health Initiative (CHHI). The implementation focus of the CHHI on building capacity for heart health promotion is paralleled by efforts to measure capacity. Through the collective experience of Heart Health Programs in Nova Scotia, Saskatchewan, Alberta and British Columbia, critical issues in measuring health promotion capacity are identified and strategies for addressing them are presented. The provincial contexts for the programs vary, as do the conceptualizations of capacity and intervention strategies to build capacity. Yet, despite such differences across provinces, shared issues influencing measuring capacity number many. These include: multiple understandings of terms; evolving understanding of capacity; invisibility of capacity building; detecting change within a dynamic system; staff turnover; time course required for change; attribution for change in capacity; understanding a process through 'snap-shot' measurements; lack of existing 'gold standard' measurement tools; validity and credibility of instruments; evolving nature of measurement tools; gathering perspectives from multiple levels within organizations; dealing with conflicting perspectives; and managing and disseminating sensitive data. A number of strategies have been devised or adopted to address measurement issues, ranging from adopting participatory processes to the development of monitoring systems. Understanding and addressing issues in measuring capacity deserve attention as they may be potent influences in the dynamic interplay between research and intervention in the process of capacity building in the context of health promotion generally, and/or heart health specifically.
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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 | 0.028 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.018 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".