Programme d’activité physique et troubles graves de santé mentale : étude de cas d’une équipe communautaire de traitement intensif (ÉCTI)
Bibliographic record
Abstract
Context : As a therapeutic intervention, physical activity has the potential to improve the quality of life of individuals with severe mental illnesses.Objectives : The goal of this case study was to conduct an in-depth examination of an individualized physical activity program for patients suffering from severe mental illnesses that was implemented by an Assertive Community Treatment (ACT) team in Ottawa, Canada.Method : Using a mixed-methods design, physical health parameters were measured over a nine-month period and semi-structured interviews were conducted with fourteen patients and five staff members.Results : The findings showed a significant reduction in weight following the evaluation period, as well as positive effects in terms of patients' self-esteem, autonomy, and socialization. The quality of the therapeutic relationship, the elimination of barriers, and the continued involvement of staff members were some of the key characteristics that led to the program's success.Discussion/conclusion : These promising results are an indication of the feasibility of this type of intervention among patients with severe mental illnesses as a therapeutic approach to improve their quality of life and support their recovery and social integration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".