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Record W2324501000 · doi:10.1037/h0094985

Psychiatric rehabilitation education for physicians.

2013· article· en· W2324501000 on OpenAlexaffabout
Abraham Rudnick, Diane Eastwood

Bibliographic record

VenuePsychiatric Rehabilitation Journal · 2013
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPsychosocialCertificationRehabilitationMental healthMedical educationNursingMedicinePsychologyPsychiatryPolitical science

Abstract

fetched live from OpenAlex

As part of a rapidly spreading reform toward recovery-oriented services, mental health care systems are adopting Psychiatric/Psychosocial Rehabilitation (PSR). Accordingly, PSR education and training programs are now available and accessible. Although psychiatrists and sometimes other physicians (such as family physicians) provide important services to people with serious mental illnesses and may, therefore, need knowledge and skill in PSR, it seems that the medical profession has been slow to participate in PSR education. Based on our experience working in Canada as academic psychiatrists who are also Certified Psychiatric Rehabilitation Practitioners (CPRPs), we offer descriptions of several Canadian initiatives that involve physicians in PSR education. Multiple frameworks guide PSR education for physicians. First, guidance is provided by published PSR principles, such as the importance of self-determination (www.psrrpscanada.ca). Second, guidance is provided by adult education (andragogy) principles, emphasizing the importance of addressing attitudes in addition to knowledge and skills (Knowles, Holton, & Swanson, 2011). Third, guidance in Canada is provided by Canadian Medical Education Directives for Specialists (CanMEDS) principles, which delineate the multiple roles of physicians beyond that of medical expert (Frank, 2005) and have recently been adopted in Australia (Boyce, Spratt, Davies, & McEvoy, 2011).

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.342
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.050
GPT teacher head0.413
Teacher spread0.363 · 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

Citations10
Published2013
Admission routes2
Has abstractyes

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