Exploring the Impact of Community-Based Arts Programming on Determinants of Health using Secondary Evaluation Data
Bibliographic record
Abstract
Arts Health Antigonish! (AHA!) is a not–for- profit community organization whose mandate is to foster creative expression for community health and well-being (www.artshealthantigonish.org). Over a four-year period, AHA! programs have engaged approximately 20 local artists and over 2000 community members through poetry, visual arts, dance and music, drama, and digital storytelling. As part of an effort to plan sustainable growth, AHA! completed a summary evaluation of six of its major programs. Programs selected for this evaluation had been offered to a specific group of people on an ongoing basis for a minimum of three months and comparable evaluation data was available. The summary confirmed that participants in all six programs experienced increased social inclusion and meaningful relationships. Marked improvements were noted in health care and living environments and education outcomes. Many positive outcomes around individual development were also identified, such as positive self-expression, improved self-confidence, belonging and empathy. Assessing the impact of broader structural determinants of health remains a challenge. These findings provide direction for future planning, evaluation, and knowledge sharing approaches.
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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 | 0.085 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".