Students' perspectives on digital psychotherapy - possible solutions for digital inpatient-like care concepts: a qualitative interview study (Preprint)
Bibliographic record
Abstract
Abstract Background The demand for mental health treatment is increasing, while the availability of treatment remains insufficient to meet the rising demand. Alternative solutions need to be explored to enable access to care for patients who cannot participate in traditional psychotherapeutic settings due to common barriers like place of residence, professional obligations, or physical limitations. Objective This study aimed to investigate attitudes toward digital psychotherapy, specifically within a digital inpatient-like therapy setting, among psychology and medical students. These students represent the future generation of therapists and possess the educational background necessary to develop innovative ideas to benefit a digital psychotherapeutic setting. Methods We conducted qualitative, semistructured interviews with 20 participants (10 psychology students and 10 medical students). The data were analyzed using an inductive, thematic analysis according to the methodology outlined by Braun and Clarke. Results The thematic analysis led to a codebook including 4 overarching categories: (1) evolution of digitalization in medical practice, (2) future directions for digital psychotherapy, (3) technical framework, and (4) artificial intelligence–based psychotherapy. Conclusions In the context of mental health, digital psychotherapy is accepted as a viable option when conventional face-to-face therapy is not possible. The primary concerns were potential impairments in the therapeutic relationship and interaction. Artificial intelligence was rejected as a standalone therapy but was considered acceptable as a supplementary tool. Technical problems represent a major obstacle for the consistent and reliable implementation of digital psychotherapy. A successful digital psychotherapeutic concept for inpatient and outpatient settings needs to enable a sufficient interpersonal therapeutic relationship situated within a reliable technical framework.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".