Connections First: Community perceptions of social connections
Bibliographic record
Abstract
Introduction: Community-based social connections and natural supports promote well-being in children and youth, and their families. Natural supports are informal reciprocal connections that consist of close relationships with friends and family, and broader associations, including neighbours, organizations and local businesses. Similarly to social connections, natural supports aim to create supportive and healthy environments with an emphasis on locality. This study investigated how urban communities are working to promote natural supports to address vulnerability. Methods: Using classical grounded theory, community champions were interviewed regarding their knowledge and perceptions of natural supports strategies, and key facilitators and barriers. Categories, themes and sub-themes were identified, and a theory emerged. Results: The overarching theory that emerged to advance natural supports strategies in urban community settings was: Building a community’s ability to shift from disconnected to naturally supportive to empower residents and families: the need for action to accelerate connection and asset development. Community connectors and assets facilitate natural supports strategies and social connections within urban community settings. Limited access to space, and difficulties recruiting and retaining volunteers were identified barriers. Conclusion: The findings of this study enable knowledge users, such as planners and policy-makers, to optimally invest and develop community natural supports strategies to enhance social connections and remediate vulnerability for children and youth, and their families. Future directions of this study include implementation and evaluation of natural supports strategies within 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".