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Record W2134939899 · doi:10.1192/pb.bp.112.042457

Academic training in psychiatry

2013· article· en· W2134939899 on OpenAlexaboutno aff
Çlare Oakley, Emma L. West, Ian Jones

Bibliographic record

VenueThe Psychiatrist · 2013
Typearticle
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsnot available
FundersNational Institute for Health and Care Research
KeywordsFlexibility (engineering)Medical educationPsychologyAcademic institutionInstitutionLearning developmentClinical psychiatryPsychiatryAcademic yearTraining (meteorology)Quarter (Canadian coin)Quality (philosophy)MedicineHigher educationLibrary sciencePolitical scienceManagement

Abstract

fetched live from OpenAlex

Aims and method The structure of academic training in psychiatry has changed in recent years and little is known about the trainees currently pursuing this career path. Two surveys were conducted of academic trainees in psychiatry and the heads of departments of psychiatry. These surveys aimed to identify the number of trainees currently in academic training, the nature of their positions and opinions about the current system of training in academic psychiatry. Results There were 165 academic trainees identified, of whom 101 were not currently in academic clinical fellow (ACF) or academic clinical lecturer (ACL) posts. Academic trainees are located in a relatively small number of universities, with a quarter being based at one institution. In total, 60% of the trainees were in general adult psychiatry. Only 4.6% of respondents rated their academic training as excellent and just over half were certain that they wished to pursue an academic career in the future. Various challenges to academic training in psychiatry were identified by both the heads of departments and trainees. Clinical implications Current difficulties in academic training in psychiatry, such as lack of flexibility of the training pathway, need addressing to ensure the provision of high-quality research and teaching in psychiatry in the future.

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)0.003
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.622
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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 science0.0000.000
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 head0.129
GPT teacher head0.436
Teacher spread0.307 · 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 designNot applicable
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
Published2013
Admission routes1
Has abstractyes

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