Mental health mobile apps for patients: Psychiatrists’ concerns
Bibliographic record
Abstract
Introduction The use of mobile apps aimed at supporting patients with a mental illness is rapidly increasing. Objectives The presented results explore psychiatrists’ concerns about mobile apps for patients with a mental illness. These results are part of a larger study that examines psychiatrists’ attitudes regarding the use and development of mobile apps. Methods In the qualitative exploratory study, semi-structured interviews were conducted with 18 psychiatrists in Czech Republic, Austria, and Slovakia. Psychiatrists were recruited via snowball sampling. The interviews were digitally recorded, transcribed verbatim, translated into English, and content analyzed using deductive and inductive category development. Results There were mixed feelings regarding mobile apps for patients with mental illness. While psychiatrists emphasized certain benefits (e.g. increasing patients’ treatment motivation and engagement), several concerns were also expressed, especially by psychiatrists who were generally unfamiliar with mobile apps. They feared being replaced; were afraid that patients would act as their own doctors, thereby damaging their health; stressed that mobile apps could not respond or be tailored to an individual the same way psychiatrists could tailor treatment to a patient. Conclusions The psychiatrists who were more likely to have concerns about mental health apps were those who were generally unfamiliar with the apps and/or thought the apps aim to replace, rather than support, face-to-face treatment. Thus, clinicians and patients should be familiarized with the use of such mobile apps and educated on how they could support the face-to-face treatment.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".