Measuring the Progress of Capacity Building in the Alberta Policy Coalition for Cancer Prevention
Bibliographic record
Abstract
The Alberta Policy Coalition for Cancer Prevention (APCCP) represents practitioners, policy makers, researchers, and community organizations working together to coordinate efforts and advocate for policy change to reduce chronic diseases. The aim of this research was to capture changes in the APCCP's capacity to advance its goals over the course of its operation. We adapted the Public Health Agency of Canada's validated Community Capacity-Building Tool to capture policy work. All members of the APCCP were invited to complete the tool in 2010 and 2011. Responses were analyzed using descriptive statistics and t tests. Qualitative comments were analyzed using thematic content analysis. A group process for reaching consensus provided context to the survey responses and contributed to a participatory analysis. Significant improvement was observed in eight out of nine capacity domains. Lessons learned highlight the importance of balancing volume and diversity of intersectoral representation to ensure effective participation, as well as aligning professional and economic resources. Defining involvement and roles within a coalition can be a challenging activity contingent on the interests of each sector represented. The participatory analysis enabled the group to reflect on progress made and future directions for policy advocacy.
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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 | 0.048 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".