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Assessing Assessing! An Examination of the Impact of the New RANZCP by-Laws for Training

2003· article· en· W2107329244 on OpenAlexaboutno aff
Gin S. Malhi, Diana McKay

Bibliographic record

VenueAustralasian Psychiatry · 2003
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsFlexibility (engineering)Set (abstract data type)Training (meteorology)Medical educationPsychologyOral examinationLawMedicinePolitical scienceFamily medicineManagementComputer scienceGeography

Abstract

fetched live from OpenAlex

Objectives: With the Royal Australian and New Zealand College of Psychiatrists’ new by-laws for training coming into effect in late 2003 and early 2004 for New Zealand and Australian trainees, respectively, the authors set out to determine the impact of the various proposed changes upon assessment. Methods: The relevant medical databases and websites were explored and the various colleges and examination boards in Australia and New Zealand, the UK and Canada were consulted. Results: The Fellowship examination in Australia and New Zealand is due to undergo significant changes in terms of its structure and methods of assessment. The changes borrow components from contemporary international systems and aim to shift the emphasis of training and examination to earlier in the postgraduate years, with trainees ultimately having greater flexibility in terms of deciding when to sit for these. As yet, there is little specific information for candidates as regards the exact format and content of some components of the examinations and it is unclear whether the new system has been evaluated sufficiently. Conclusions: Many of the proposed changes bear similarities to training structures and examination systems in Canada and the UK. It is suggested that the ‘mistakes’ of others should be learned from, and that a considered and deliberate approach is taken to the introduction of new examinations, appraising their merit and acknowledging the difficulties that change and uncertainty create.

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)0.001
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 score0.574
Threshold uncertainty score0.340

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.042
GPT teacher head0.402
Teacher spread0.360 · 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

Citations2
Published2003
Admission routes1
Has abstractyes

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