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Record W2309485266 · doi:10.1177/0706743715625952

Career Interests of Canadian Psychiatry Residents: What Makes Residents Choose a Research Career?

2016· article· en· W2309485266 on OpenAlexaffvenueabout
Vincent Laliberté, Mark Rapoport, Melissa Andrew, Marla Davidson, Soham Rej

Bibliographic record

VenueThe Canadian Journal of Psychiatry · 2016
Typearticle
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsUniversity of SaskatchewanUniversity of TorontoSunnybrook Health Science CentreQueen's UniversityHealth Sciences CentreMcGill University
Fundersnot available
KeywordsResidency trainingMedical educationVocational educationMedicinePsychologyFamily medicinePsychiatryContinuing educationPedagogy

Abstract

fetched live from OpenAlex

OBJECTIVES: Training future clinician-researchers remains a challenge faced by Canadian psychiatry departments. Our objectives were to determine the prevalence of residents interested in pursuing research and other career options as part of their practice, and to identify the factors associated with interest in research. METHOD: Data from a national online survey of 207 Canadian psychiatry residents from a total of 853 (24.3% response rate) were examined. The main outcome was interest in research as part of residents' future psychiatrist practice. Bivariate and multivariate analyses were performed to identify demographic and vocational variables associated with research interest. RESULTS: Interest in research decreases by 76% between the first and fifth year of psychiatry residency (OR 0.76 per year, 95% CI 0.60 to 0.97). Training in a department with a residency research track did not correlate with increased research interest (χ2 = 0.007, df = 1, P = 0.93). CONCLUSIONS: Exposing and engaging psychiatry residents in research as early as possible in residency training appears key to promoting future research interest. Psychiatry residency programs and research tracks could consider emphasizing research training initiatives and protected research time early in residency.

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)0.007
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.569
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.183
GPT teacher head0.417
Teacher spread0.234 · 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

Citations13
Published2016
Admission routes3
Has abstractyes

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