Working with Toronto neighbourhoods toward developing indicators of community capacity
Bibliographic record
Abstract
Often the goal of health and social development agencies is to assess communities and work with them to improve community capacity. Particularly for health promoters working in community settings and to ensure consistency in the definition of health promotion, the evaluation of health promotion programmes should be based on strengths and assets, yet existing information for planning and evaluation purposes usually focuses on problems and deficits. A model and definition of community capacity, grounded in community experience and focusing on strengths and assets, was developed following a 4-year, multi-site, qualitative, action research project in four Toronto neighbourhoods. There was significant community involvement in the four Community Advisory Committees, one for each study site. Semi-structured, open-ended interviews and focus groups were conducted with 161 residents and agency workers identified by the Community Advisory Committees. The data were analyzed with the assistance of NUDIST software. Thematic analysis was undertaken in two stages: (i) within each site and (ii) across sites, with the latter serving as the basis for the development of indicators of community capacity. This paper presents a summary of the research, the model and the proposed indicators. The model locates talents and skills of community members in a larger context of socioenvironmental conditions, both inside and outside the community, which can act to enable or constrain the expression of these talents and skills. The significance of the indicators of community capacity proposed in the study is that they focus on identifying and measuring the facilitating and constraining socioenvironmental conditions.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.000 | 0.001 |
| 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".