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Record W2757288156 · doi:10.1080/00207411.2017.1368308

French version of the profiles of occupational engagement in people with severe mental illness: Translation, adaptation, and validation

2017· article· en· W2757288156 on OpenAlexaff
Nadine Larivière, Ginette Aubin, Marie-Ève Pépin, Vanessa Maurice, François Lavertu, Cynthia Tardif, Sandra Labbé, Ulrika Bejerholm

Bibliographic record

VenueInternational Journal of Mental Health · 2017
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsUniversité du Québec à Trois-RivièresUniversité de Sherbrooke
Fundersnot available
KeywordsInter-rater reliabilityMental illnessPsychologyOccupational therapyPsychological interventionReliability (semiconductor)Clinical psychologyApplied psychologyMental healthPsychiatryDevelopmental psychologyRating scale

Abstract

fetched live from OpenAlex

Occupational engagement is affected in many persons with severe mental illness (SMI) because of personal, occupational, and environmental issues. However, no French assessment tool measuring occupational engagement in these persons is currently available. The objectives of the study were to translate the “Profiles of Occupational Engagement in People with Severe Mental Illness” (POES), including a time use diary and an interview, and to measure the interrater reliability of the French version. First, a transcultural validation process of the English version of the POES into French was completed. Second, to determine interrater reliability, ten evaluators assessed 23 participants with an SMI. Specific concepts were clarified with the author of the original tool. No items were added or removed following the translation process. An interview guide was developed to assist the interview on occupational engagement. Analyses indicated high interrater reliability (ICC = 0.94). French-speaking clinicians will be able to use a validated French version of the POES to better capture occupational engagement in people with SMI, facilitating the individualisation of interventions.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.436

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.119
GPT teacher head0.476
Teacher spread0.357 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2017
Admission routes1
Has abstractyes

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