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Record W2762779240 · doi:10.1177/0163278717734282

Variables Associated With Perceived Work Role Performance Among Professionals in Multidisciplinary Mental Health Teams Overall and in Primary Care and Specialized Service Teams, Respectively

2017· article· en· W2762779240 on OpenAlexafffundabout
Marie‐Josée Fleury, Guy Grenier, Jean-Marie Bamvita, François Chiocchio

Bibliographic record

VenueEvaluation & the Health Professions · 2017
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of OttawaMcGill UniversityDouglas Mental Health University Institute
FundersFonds de Recherche du Québec - Santé
KeywordsContext (archaeology)Mental healthMultidisciplinary approachPsychologyTeam effectivenessService (business)Applied psychologyMultidisciplinary teamSample (material)NursingKnowledge managementMedicineBusinessMarketingPsychiatry

Abstract

fetched live from OpenAlex

This study had a dual purpose (1) to identify variables associated with perceived work role performance (WRP) among 315 mental health professionals (MHPs) in Quebec and (2) to compare variables related to WRP in MH primary care teams (PCTs) and specialized service teams (SSTs), respectively. WRP was measured using an adapted version of the work role questionnaire. Variables were organized within five areas: individual characteristics, perceived team attributes, perceived team processes, perceived team emergent states, and geographical and organizational context. Half of the WRP variables were linked to team processes. Knowledge sharing correlated with WRP in both MH PCTs and SSTs. Team attributes had more impact on MH PCTs, while team processes and team emergent states played a larger role among SSTs. The association between WRP and knowledge sharing confirms the need for a systematic training program to promote interdisciplinary collaboration. Integration strategies (e.g., service agreements) could improve collaboration between MH PCTs and SSTs and help MHPs perform more effectively within PCTs.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0080.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
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 head0.043
GPT teacher head0.435
Teacher spread0.392 · 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 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

Citations6
Published2017
Admission routes3
Has abstractyes

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