Youth’s sense of belonging and associated risk and promotive factors: An ecological systems network analysis
Bibliographic record
Abstract
Introduction Belonging is a powerful predictor of positive outcomes in youth, including greater well-being. There remains a pressing need to integrate influences across layers of organization within youths’ developmental contexts to further understand how to enhance belonging amongst this demographic. Here, we investigate: (1) “How do risk and promotive factors converge in relation to belonging among youth?” and (2) “Do risk and promotive factors associate differently with belonging between boys and girls?”. Methods Responses from a community-based questionnaire were analyzed to establish ecological systems networks of the interrelationships between youths’ social connections, well-being, belonging, and sociodemographic factors (N girls = 477, N boys = 245; M age = 14.2, SD = 2.2 years). Results Our findings demonstrate the salience of ethnicity-based discrimination experiences in diminished mental health outcomes and lower belonging among boys. Additionally, we show the crucial link between emotional support from teachers and family with higher belonging for youth. Conclusions: We discuss the importance of gender-based considerations when targeting belonging promotion and well-being among children and adolescents.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".