Conceptualization of a decision-analytic model in youth mental health: an application of stakeholder engagement in model development in the Netherlands
Bibliographic record
Abstract
BACKGROUND: The long-term impact of preventive policies in the Netherlands on the mental health of young adults remains unclear. Therefore, this paper describes the development of a conceptual model of youth mental health that serves as the foundation of a future decision-analytic model. RESEARCH DESIGN AND METHODS: Stakeholders were engaged through three rounds of focus group discussions to indicate the factors of youth mental health that affect the likelihood of developing mental disorders later in life and the relationships among them. Findings were discussed with stakeholders and in a study team that included members with diverse backgrounds. Literature was used as an additional information source for the relationships among the selected factors. RESULTS: In total, 43 stakeholders participated in the focus group discussions. Eleven factors of youth mental health were regarded as most influential, with 13 relationships among them. The final conceptual model was approved by the stakeholders and the study team. CONCLUSIONS: Through integrating stakeholder perspectives and published literature, a conceptual model was created that captures essential factors and relationships affecting (long-term) mental health. Although stakeholder engagement requires extensive planning, it enhanced the model's credibility and validity, and could therefore serve as a complement to other conceptual modeling approaches.
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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 | 0.033 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".