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Record W2744192312 · doi:10.1177/2050312117719093

Variables associated with work performance in multidisciplinary mental health teams

2017· article· en· W2744192312 on OpenAlexaffabout
Marie‐Josée Fleury, Guy Grenier, Jean-Marie Bamvita, François Chiocchio

Bibliographic record

VenueSAGE Open Medicine · 2017
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of OttawaMcGill UniversityDouglas Mental Health University Institute
Fundersnot available
KeywordsClanMental healthMultidisciplinary approachOrganizational cultureWork (physics)MedicineOrganizational performanceKnowledge managementHierarchyApplied psychologyPsychologyPublic relationsPsychiatryComputer scienceEngineering

Abstract

fetched live from OpenAlex

OBJECTIVES: This study investigates work performance among 79 mental health teams in Quebec (Canada). We hypothesized that work performance was positively associated with the use of standardized clinical tools and clinical approaches, integration strategies, "clan culture," and mental health funding per capita. METHODS: Work performance was measured using an adapted version of the Work Role Questionnaire. Variables were organized into four key areas: (1) team attributes, (2) organizational culture, (3) inter-organizational interactions, and (4) external environment. RESULTS: Work performance was associated with two types of organizational culture (clan and hierarchy) and with two team attributes (use of standardized clinical tools and approaches). DISCUSSION AND CONCLUSION: This study was innovative in identifying associations between work performance and best practices, justifying their implementation. Recommendations are provided to develop organizational cultures promoting a greater focus on the external environment and integration strategies that strengthen external focus, service effectiveness, and innovation.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.301
Threshold uncertainty score0.599

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.044
GPT teacher head0.466
Teacher spread0.423 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2017
Admission routes2
Has abstractyes

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