Mental Health Therapy Protocols and eHealth Design: Focus Group Study
Bibliographic record
Abstract
BACKGROUND: Electronic health (eHealth) programs are often based on protocols developed for the original face-to-face therapies. However, in practice, therapists and patients may not always follow the original therapy protocols. This form of personalization may also interfere with the intended implementation and effects of eHealth interventions if designers do not take these practices into account. OBJECTIVE: The aim of this explorative study was to gain insights into the personalization practices of therapists and patients using cognitive behavioral therapy, one of the most commonly applied types of psychotherapy, in a youth addiction care center as a case context. METHODS: Focus group discussions were conducted asking therapists and patients to estimate the extent to which a therapy protocol was followed and about the type and reasons for personalization of a given therapy protocol. A total of 7 focus group sessions were organized involving therapists and patients. We used a commonly applied protocol for cognitive behavioral therapy as a therapy protocol example in youth mental health care. The first focus group discussions aimed at assessing the extent to which patients (N=5) or therapists (N=6) adapted the protocol. The second focus group discussions aimed at estimating the extent to which the therapy protocol is applied and personalized based on findings from the first focus groups to gain further qualitative insight into the reasons for personalization with groups of therapists and patients together (N=7). Qualitative data were analyzed using thematic analysis. RESULTS: Therapists used the protocol as a "toolbox" comprising different therapy tools, and personalized the protocol to enhance the therapeutic alliance and based on their therapy-provision experiences. Therapists estimated that they strictly follow 48% of the protocol, adapt 30%, and replace 22% by other nonprotocol therapeutic components. Patients personalized their own therapy to conform the assignments to their daily lives and routines, and to reduce their levels of stress and worry. Patients estimated that 29% of the provided therapy had been strictly followed by the therapist, 48% had been adjusted, and 23% had been replaced by other nonprotocol therapeutic components. CONCLUSIONS: A standard cognitive behavioral therapy protocol is not strictly and fully applied but is mainly personalized. Based on these results, the following recommendations for eHealth designers are proposed to enhance alignment of eHealth to therapeutic practice and implementation: (1) study and copy at least the applied parts of a protocol, (2) co-design eHealth with therapists and patients so they can allocate the components that should be open for user customization, and (3) investigate if components of the therapy protocol that are not applied should remain part of the eHealth applied. To best generate this information, we suggest that eHealth designers should collaborate with therapists, patients, protocol developers, and mental health care managers during the development process.
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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 | 0.093 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".