Understanding Mental Health Professionals’ Perspectives and Practices Regarding the Implementation of Digital Mental Health: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Despite the potential of digital mental health to provide cost-effective mental health care, its adoption in clinical settings is limited, and little is known about the perspectives and practices of mental health professionals regarding its implementation or the factors influencing these perspectives and practices. OBJECTIVE: This study aims to characterize in depth the perspectives and practices of mental health professionals regarding the implementation of digital mental health and explore the factors affecting such perspectives and practices. METHODS: A qualitative study using in-depth semistructured interviews with Portuguese mental health professionals (N=13)-psychologists and psychiatrists-was conducted. The transcribed interviews were thematically analyzed. RESULTS: Mental health professionals deemed important or engaged in the following practices during the implementation of digital mental health: indication evaluation, therapeutic contract negotiation, digital psychological assessment, technology setup and management, and intervention delivery and follow-up. Low-threshold accessibility and professionals' perceived duty to provide support to their clients facilitated the implementation of digital mental health. Conversely, the lack of structured intervention frameworks; the unavailability of usable, validated, and affordable technology; and the absence of structured training programs inhibited digital mental health implementation by mental health professionals. CONCLUSIONS: The publication of practice frameworks, development of evidence-based technology, and delivery of structured training seem key to expediting implementation and encouraging the sustained adoption of digital mental health by mental health professionals.
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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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 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".