Exploring Qualitative Evidence Through a School-Based Intervention: Can Youth Be Empowered Through Writing?
Bibliographic record
Abstract
Background: The mental health crisis among youth, exemplified by the 1.2 million affected in Canada, underscores the urgent need for effective interventions. Research indicates that in-school programs are more successful in addressing common mental health issues and promoting well-being. This evaluation study was performed on the Kids Write Network, an expressive writing intervention that aims to facilitate emotional expression and processing by encouraging individuals to address and reassess adverse life events through writing. This study explores how the cognitive and psychological mechanisms of expressive writing in adolescents can be unveiled through the theoretical approach of empowerment. Methods: Using a multiple case-study design, 50 middle school students from three schools in Montreal, Canada, participated in the expressive writing intervention and were individually interviewed. The data collected from these interviews were analyzed using a comprehensive coding schema to identify the theoretical underpinnings of empowerment. Results: The results demonstrate that writing about one’s negative life event allowed for a better understanding of it through reflection and a clearer understanding of one’s emotions. Conclusions: The findings can inform the development and implementation of school-based empowerment interventions for youth with mental health challenges. Further, our findings emphasize the importance of recognizing and addressing the theoretical approach of empowerment in mental health programs for young individuals.
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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 | 0.088 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".