Asset-based community development for health promotion in culturally defined communities
Bibliographic record
Abstract
As our understanding of the broader determinants of health such as socioeconomic status and the social environment has matured, community development has emerged as a possible health promotion strategy. This thesis explores Kretzmann and McKnight's asset-based community development (ABCD) (Kretzmann & McKnight, 1993), a strengths based community development strategy, as a potential method of promoting health in culturally defined communities. The specific research question is: Is ABCD a suitable strategy for promoting health in culturally defined communities? Where a "suitable strategy": reflects health promotion and population health research; addresses the specific challenges associated with promoting health in culturally defined communities; and, is of practical use in terms of helping communities identify their health issues, develop plans to address these issues, implement these plans, and evaluate the plans. Together, these three "suitability dimensions" incorporate both theoretical and practical expectations. The evaluation is accomplished through a cross-disciplinary literature review, individual interviews, and a focus group with key informants who are knowledgeable of health promotion issues in Vancouver's Chinese communities. The information gathered through these interviews and the focus group is incorporated into the thesis as a small case study of ABCD's potential in Chinatown/Strathcona and Richmond Chinese communities. This evaluation suggests that on balance ABCD may be an appropriate approach for promoting health in culturally defined communities. Although the strategy lacks clear direction in terms of evaluation processes, may not help communities identify their health issues, and faces several challenges associated with power imbalances, these weaknesses are outweighed by its strengths, which are its focus on assets, emphasis on community empowerment, and reflection of the socio-ecological approach to health. For these same reasons, ABCD also shows potential as a health promotion strategy in both Chinatown/Strathcona and Richmond Chinese communities. While these conclusions are favourable, further research on evaluation of community-based health promotion programs and on health issues in culturally defined communities would improve ABCD's suitability as a health promotion strategy.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".