L’assistant personnel numérique : outil de soutien à la réadaptation en santé mentale
Bibliographic record
Abstract
BACKGROUND: People living with mental illness may manifest cognitive and social-emotional difficulties leading to several challenges in their daily lives. Using technological aids may help to compensate for some of these difficulties. PURPOSE: The objective of this study was to document the effects and issues of the use of mobile technology applications (apps) with people with mental illness during the rehabilitation process. METHOD: Using an exploratory qualitative evaluative approach, 12 participants were engaged in ongoing brief interviews in which they discussed their use of a variety of apps that met their rehabilitation needs. A thematic analysis (descriptive interpretive) was used to uncover the effects and issues of the integration of apps in participants' daily life. FINDINGS: The apps helped participants to overcome their cognitive difficulties, facilitated the management of their daily tasks and socialization, and prevented boredom. However, several issues related to the context, the technology, and the person need to be considered. IMPLICATIONS: The findings prompt one to consider the therapist's role in the integration of personal digital assistants in psychosocial rehabilitation.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".