Addressing Teen Mental Health Needs at Individual and Community Levels
Bibliographic record
Abstract
The study explored how to support teen resilience by examining the experiences of adolescents in the U.S. and Ukraine. Semi-structured interviews with parents from the U.S. and Ukraine were used to investigate the emotional distress experienced by adolescents and the resilience strategies and resources they use. Ukrainian and U.S. caregivers’ reports share many similarities and demonstrate the importance of community institutions in supporting teens. Findings suggest that libraries can support adolescents by offering curated content and mental health assistance and by providing safe spaces (digital and physical) to obtain information and socialize with peers. Répondre aux besoins des adolescents en matière de santé mentale au niveau individuel et communautaire RésuméL'étude s'est penchée sur la manière de soutenir la résilience chez les jeunes en examinant les expériences des adolescents aux États-Unis et en Ukraine. Des entretiens semi-structurés avec des parents américains et ukrainiens ont permis d'étudier la détresse émotionnelle des adolescents ainsi que les stratégies de résilience et les ressources qu'ils utilisent. Les rapports des parents ukrainiens et américains présentent de nombreuses similarités et démontrent l'importance des institutions communautaires dans le soutien aux adolescents. Les résultats suggèrent que les bibliothèques peuvent soutenir les adolescents en proposant des contenus adaptés et une assistance en matière de santé mentale et en offrant des espaces sûrs (virtuels et physiques) pour obtenir des informations et socialiser avec des pairs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".