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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".