Finding a Needle in a Haystack: A Systematic Approach for Searching Through Public Databases for Youth Mental Well‐Being Programs
Bibliographic record
Abstract
BACKGROUND: This practice paper exemplifies a systematic approach used to learn about existing mental well-being programs for youth 11-14 years to inform curriculum development for after-school settings. METHODS: We reviewed 3389 mental well-being programs from publicly accessed databases and conducted a content analysis using inductive and deductive coding to explore the domains each program addressed. RESULTS: Through our content analysis of the final eight programs, we found strong alignment with the Collaborative for Academic, Social and Emotional Learning (CASEL) core social-emotional competencies: self-awareness, self-management, social awareness, relationship skills, and decision-making. IMPLICATIONS FOR PRACTICE: Although using established processes (e.g., PICO, CFIR) to review public databases is an effective research strategy, engaging in research-intensive endeavors is time consuming and may not be practical for after-school administration. The benefits of community-academic partnerships, such as EMPOWER, are highlighted as an approach, and opportunity, to promote evidence-based research practices to inform programming in community organizations. CONCLUSION: Enhancing youth social emotional competencies is an important means to supporting youth mental well-being. Incorporating a systematic approach to select youth mental well-being programs provides a structure, for our EMPOWER project, that can steer the choice of curricula to meet the needs of after-school program contexts.
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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 | 0.244 | 0.464 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.008 |
| Bibliometrics | 0.103 | 0.049 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.008 | 0.015 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".