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Record W2003591688 · doi:10.3109/13561820.2011.554240

Supporting front-line practitioners' professional development and job satisfaction in mental health and addiction

2011· article· en· W2003591688 on OpenAlexafffund
Marion Bogo, Jane Paterson, Lea Tufford, Regine King

Bibliographic record

VenueJournal of Interprofessional Care · 2011
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsCentre for Addiction and Mental HealthUniversity of Toronto
FundersUniversity of Toronto
KeywordsFront lineMental healthCompetence (human resources)Job satisfactionNursingPsychological interventionAccountabilityPsychologyMedicineMedical educationPolitical science

Abstract

fetched live from OpenAlex

Substantial organizational change in many health institutions has eliminated profession-based departments and replaced them with program management structures. This article aims to explore practitioners' perceptions of their professional work in a large urban centre for addiction and mental health that has undergone such change. Seventy-six practitioners from six professions participated in focus groups that were transcribed and analyzed. Practitioners' perceptions about their professional competence, performance, development, and job satisfaction were affected by three interrelated factors: available supervision from experts who validate practitioners' subjective work experiences and provide population-specific knowledge for effective interventions; teams that provide a home base and support through positive interpersonal relations, collaboration and informal feedback; and organizations and managers who provide assistance and training while expecting quality performance and productivity. Effective clinical and organizational leaders manage tensions between providing supportive environments and expecting accountability throughout the workplace.

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)0.001
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 score0.148
Threshold uncertainty score0.576

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.107
GPT teacher head0.436
Teacher spread0.330 · 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

Citations39
Published2011
Admission routes2
Has abstractyes

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