Fostering Rural Youth Wellbeing through Afterschool Programs: The Case of Fusion Youth and Technology Centre, Ingersoll, Ontario
Bibliographic record
Abstract
Rural youth face many challenges and risks to their wellbeing. One means of mitigating the risks rural youth experience, is through the provision of afterschool programs. This study reports on adult perceptions of how participation in an afterschool program—Fusion Youth and Technology Centre—affects rural youth wellbeing. A qualitative study was undertaken in which nine staff members, three program administrators, and six knowledgeable adult community members were interviewed to determine how they perceived the impact that participation in Fusion had on the wellbeing of rural youth. Three broad themes were identified that contributed to enhanced rural youth wellbeing: (1) engaging youth through an eclectic mix of programs and activities; (2) building relationships and connections; and (3) a place for youth. The results of this study were then contrasted with an earlier study that examined the perceptions of youth who participated in Fusion. There was a corroboration of all themes between studies with the exception of enhanced community relationships. The conclusion is that participating in Fusion Youth and Technology Centre contributes positively to the wellbeing of rural youth. This then raises a number of questions that rural communities need to ask themselves if they are serious about promoting the wellbeing of the youth in their communities. Keywords: youth, rural, health, wellbeing, afterschool programs
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.020 | 0.003 |
| Scholarly communication | 0.002 | 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".