Analysis of the Recomposition of Norms and Representations in the Field of Psychiatry and Mental Health in the Age of Electronic Mental Health: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: For the World Health Organization, electronic health (eHealth) is seen as an effective way to improve therapeutic practices and disease prevention in health. Digital tools lead to major changes in the field of mental medicine, but specific analyses are required to understand and accompany these changes. OBJECTIVE: Our objective was to highlight the positions of the different stakeholders of the mental health care system on eHealth services and tools, as well as to establish professional and user group profiles of these positions and the uses of these services. METHODS: In order to acquire the opinions and expectations of different categories of people, we carried out a qualitative study based on 10 focus groups (n=70, from 3-12 people per group) composed of: general practitioners, psychiatrists, psychologists, social workers, occupational therapists, nurses, caregivers, mental health services users, user representatives, and the general public. The analyses of focus group discussions were performed independently by four investigators through a common analysis grid. The constant comparative method was adopted within this framework. RESULTS: The interviewees expressed different problems that new technologies engender in the field of mental health. What was previously strictly under the jurisdiction of physicians now tends to be fragmented and distributed over different groups and locations. New technologies reposition care in the field of domestic, rather than therapeutic, activities, and thus the conception of care as an autonomous activity in the subject's life is questioned. The ideal of social autonomy through technology is part of the new logic of health democracy and empowerment, which is linked to a strong, contemporary aspiration to perform. Participants emphasized that there was the potential risk of a decrease in autonomy for the digitally engaged patient, while personal empowerment could become a set of obligations. CONCLUSIONS: This qualitative research highlights the heterogeneity of opinions among the groups and within each group. It suggests that opinions on electronic mental health devices are still far from being stabilized, and that a change management process should be set up to both regulate the development and facilitate the use of these tools.
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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 | 0.060 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".