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Record W2988928624 · doi:10.3917/rsi.138.0029

Programme d’activité physique et troubles graves de santé mentale : étude de cas d’une équipe communautaire de traitement intensif (ÉCTI)

2019· article· fr· W2988928624 on OpenAlexaffabout
Eva Guérin, Jean-Pierre Dupuis, Jean Daniel Jacob, Denis Prud’homme

Bibliographic record

VenueRecherche en soins infirmiers · 2019
Typearticle
Languagefr
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of OttawaMontfort HospitalInstitut du Savoir Montfort
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.687
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.183
GPT teacher head0.431
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2019
Admission routes2
Has abstractyes

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