Building social capital as a pathway to success: community development practices of an early childhood intervention program in Canada
Bibliographic record
Abstract
In the last three decades, various concepts and strategies have been developed to address social determinants of health. This paper brings together the different focuses of health promotion, and demonstrates that effective health intervention programs need to be conducted at multiple levels and fronts. Specifically, based on the evaluation of KidsFirst, an early childhood intervention program in Saskatchewan, Canada, this paper presents the program practices effective in enhancing the social capital and social cohesion at the community and institutional levels. The findings fall into three interconnected areas: strengthening community fabric; building institutional social capital and bonding, linking and bridging. KidsFirst has brought the community together through conducting broad and targeted community consultations, and developing partnerships and collaborative relationships in an open and transparent manner. It has also developed institutional social capital through hiring locally and encouraging staff to deepen connections with the communities. Additionally, it has endeavoured to create conditions that enable vulnerable families to enhance connectedness among themselves, link them to services and integrate them to the larger community. The program's success, however, depends not only on the program's local practices, but also on the government's central policy framework and commitment. In particular, the program's focus on children's healthy development easily resonated with local communities. Its endorsement of local and intersectoral leadership has facilitated mobilizing community resources and knowledge. Further, its commitment to local ownership of the program and structural flexibility has also determined the extent to which the program could fit into the histories of local communities.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.030 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".