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Record W6999733659

Developing leadership capacity for enhancing the professional education of multidisciplinary mental health practitioners

2009· other· en· W6999733659 on OpenAlexaboutno aff

Bibliographic record

VenueUSC Research Bank (University of the Sunshine Coast) · 2009
Typeother
Languageen
FieldEngineering
TopicTransport and Logistics Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsMultidisciplinary approachMental healthProfessional developmentWork (physics)Adaptation (eye)Service delivery frameworkService (business)Collaborative modelMental health service
DOInot available

Abstract

fetched live from OpenAlex

There have been a number of studies overseas investigating collaborative inter-professional educational models for preparing mental health professionals who work in integrated or multidisciplinary mental health services. Recent implementations include the Canadian Collaborative Mental Health Initiative Tool kit, and the NHS Quality Improvement Scotland Standards for Integrated Care Pathways for Mental Health. Integrated and multi-disciplinary models of primary health service delivery have been shown to be effective and suitable for delivery of services in regional and remote rural areas and for Indigenous clients in Australia. The key strengths of multidisciplinary mental health service models may be attributed to their client focus and adaptation to complexity. The overarching aim of this Australian Learning and Teaching funded project is to develop effective, collaborative, cross-disciplinary leadership frameworks for university learning and teaching which enhance the professional preparation of the multidisciplinary mental health workforce. We are developing strategies that will prepare students in professional health training programs to be better equipped to work in multidisciplinary settings. This paper presents an overview of the project and the results of the first sets of interdisciplinary workshops.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.440
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.148
GPT teacher head0.349
Teacher spread0.200 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2009
Admission routes1
Has abstractyes

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