Defining Community Boundaries in Health Promotion Research
Bibliographic record
Abstract
PURPOSE: A means for integrating subjective experience in the operationalization of community boundaries is described and examined, and a community typology incorporating both psychosocial and structural resources is developed and applied. DESIGN: Small-area sense of belonging was used to delineate broader community boundaries, which were compared with administrative boundaries. Community differences on participation and health were analyzed by using analysis of variance and post hoc tests. SETTING: Data were from face-to-face interviews with residents of a relatively disadvantaged area of a medium-sized Canadian city. SUBJECTS: A sample of 910 individuals was drawn from a population listing of those aged 35 to 65 years in the project area (44% response rate). MEASURES: Measures include sense of belonging; income; community participation; and mental, physical, and perceived health. RESULTS: Data revealed the similarity of community boundaries based on sense of belonging with administrative boundaries. The communities differed significantly in income, community activities attended, and two health measures. The typology indicated the community rich in both income and sense of belonging had higher participation and health than did communities low in both or with mixed resources. CONCLUSIONS: Psychosocial indicators can be used to delineate community boundaries, which may be similar to administrative boundaries. A typology including both psychosocial and structural components can be a helpful preliminary step in interpreting area differences.
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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 | 0.087 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.012 |
| 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".