The Effectiveness of Healthy Community Approaches on Positive Health Outcomes in Canada and the United States
Bibliographic record
Abstract
Healthy community approaches encompass a diverse group of population based strategies and interventions that create supportive environments, foster community behavior change and improve health. This systematic review examined the effectiveness of ten most common healthy community approaches (Healthy Cities/Communities, Smart Growth, Child Friendly Cities, Safe Routes to Schools, Safe Communities, Active Living Communities, Livable Communities, Social Cities, Age-Friendly Cities, and Dementia Friendly Cities) on positive health outcomes. Empirical studies were identified through a search of the academic and grey literature for the period 2000–2014. Of the 231 articles retrieved, 26 met the inclusion criteria with four receiving moderate quality ratings and 22 poor ratings using the Effective Public Health Practice Project Quality Assessment Tool. The majority of studies evaluated Safe Routes to School Programs and reported positive associations with students’ active commute patterns. Fewer studies assessed benefits of Smart Growth, Safe Communities, Active Living Communities and Age-Friendly Cities. The remaining approaches were relatively unexplored in terms of their health benefits however focused on conceptual frameworks and collaborative processes. More robust studies with longer follow-up duration are needed. Priority should be given to evaluation of healthy community projects to show their effectiveness within the population health context.
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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 | 0.013 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".