Factors Affecting Digital Tool Use in Client Interaction According to Mental Health Professionals: Interview Study
Bibliographic record
Abstract
BACKGROUND: Digital tools and interventions are being increasingly developed in response to the growing mental health crisis, and mental health professionals (MHPs) considerably influence their adoption in client practice. However, how MHPs use digital tools in client interaction is yet to be sufficiently understood, which poses challenges to their design, development, and implementation. OBJECTIVE: This study aimed to create a contextual understanding of how MHPs use different digital tools in clinical client practice and what characterizes the use across tools. METHODS: A total of 19 Finnish MHPs participated in semistructured interviews, and the data were transcribed, coded, and inductively analyzed. RESULTS: We found that MHP digital tool use was characterized by 3 distinct functions: communication, diagnosis and evaluation, and facilitating therapeutic change. The functions were addressed using analog tools, digitized tools that mimic their analog counterparts, and digital tools that use the possibilities native to digital. The MHP-client communication included various media alongside face-to-face meetings, the MHPs increasingly used digitized tools in client evaluation, and the MHPs actively used digitized materials to facilitate therapeutic change. MHP tool use was generally characterized by adaptability-it was negotiated in client interactions. However, there was considerable variance in the breadth of MHPs' digital toolbox. The existing clinical practices emphasized MHP-client interaction and invited incremental rather than radical developments, which challenged the achievement of the scalability benefits expected from digital tools. CONCLUSIONS: MHPs use digitized and digital tools in client practice. Our results contribute to the user-centered research, development, and implementation of new digital solutions in mental health care by classifying them according to their function and medium and describing how MHPs use and do not use them.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".