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Record W2954027708 · doi:10.1177/1039856219859284

Career medical officers in psychiatry and addiction in NSW: description, role and educational needs

2019· article· en· W2954027708 on OpenAlexaboutno aff
Louise Nash, Michele Meltzer, Aspasia Karageorge

Bibliographic record

VenueAustralasian Psychiatry · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsnot available
FundersNSW Ministry of Health
KeywordsWorkforceAddictionQuarter (Canadian coin)Mental healthMedicinePsychiatryMedical educationFamily medicinePsychologyNursing

Abstract

fetched live from OpenAlex

OBJECTIVE: To describe the workforce of career medical officers (CMOs) working in psychiatry and addiction medicine across New South Wales (NSW) and to explore their training and education needs, and experience of their role. METHOD: A cross-sectional survey of CMOs in NSW working in psychiatry or addiction medicine. The survey consisted of quantitative data and free-text responses, and was conducted online in late 2017. RESULTS: Of the 41 CMOs identified and sent the survey link, 25 CMOs completed the survey (61% RR). Almost half had worked as a CMO for 11 years or longer. Only six respondents held a recognised senior CMO position. Common areas of expertise were clozapine, metabolic health, and electroconvulsive therapy (ECT). One-quarter of respondents did not receive supervision. Suggested education and training improvements included an annual 1-day training symposium and monthly peer review group for CMOs. CONCLUSION: CMOs are an often senior group of clinicians working in important areas of service provision. Ongoing educational support for this group of medical practitioners is prudent to ensure the delivery of best practice mental health and drug health care.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.020
GPT teacher head0.356
Teacher spread0.336 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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