eMental Health Resources for Youth: Parent Perceptions of AI Matched eMental Health Resources for Their Child
Bibliographic record
Abstract
Despite high prevalence rates of pediatric mental health challenges, estimates suggest over 50% of youth with mental health conditions have never accessed services. eMental health resources have increased over the past two decades to mitigate barriers to access. Positive attitudes towards eMental health resources have been reported in adults; however, little research has focused on parent perceptions of pediatric eMental health resources. The current study explored parent perceptions of matched eMental health resources for their child using machine learning algorithms. Parents were recruited from a longitudinal mental health study; 49 parents participated in a semi-structured individual virtual interview. Interpretative description analysis generated two primary themes: (1) feelings on AI use to assign matched mental health resources and (2) parent perceptions of eMental health resources for their child. Findings support a general acceptance by parents of the integration of AI in assigning and providing them with mental health resources for their child contingent on clinician oversight. This study fills a distinct gap in the literature on parent perceptions of pediatric eMental health resources and the role of AI, which are integral to consider in the development and dissemination of resources.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".