Provider Perspectives on Implementing an Enhanced Digital Screening for Adolescent Depression and Suicidality: Qualitative Study
Bibliographic record
Abstract
Background: With a growing adolescent mental health crisis, pediatric societies are increasingly recommending that primary care providers (PCPs) engage in mental health screening. While symptom-level screens identify symptoms, novel technology interventions can assist PCPs with providing additional point-of-care guidance to increase uptake for behavioral health services. Objective: In this study, we sought community PCP feedback on a web-based, digitally enhanced mental health screening tool for adolescents in primary care previously only evaluated in research studies to inform implementation in community settings. Methods: A total of 10 adolescent providers were recruited to trial the new screening tool and participate in structured interviews based on the Consolidated Framework for Implementation Research domains. Interviews were audio recorded, transcribed, and coded according to a prespecified codebook using a template analysis approach. Results: Providers identified improving mental health screening and treatment in pediatric primary care as a priority and agreed that a web-based digitally enhanced screening tool could help facilitate identification of and management of adolescent depression. Salient barriers identified were lack of electronic health record integration, time to administer screening, implications on clinic workflow, accessibility, and lack of transparency within health care organizations about the process of approving new technologies for clinical use. Providers made multiple suggestions to enhance implementation in community settings, such as incorporating customization options. Conclusions: Technology interventions can help address the need for improved behavioral health support in primary care settings. However, numerous barriers exist, complicating implementation of new technologies in real-world settings.
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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 | 0.021 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".