MétaCan
Menu
Back to cohort
Record W1975009067 · doi:10.3109/10398561003739620

Up Close and Personal: Medical Students Prefer Face-To-Face Teaching in Psychiatry

2010· article· en· W1975009067 on OpenAlexaff
Lisa Lampe, Carissa Coulston, Garry Walter, Gin S. Malhi

Bibliographic record

VenueAustralasian Psychiatry · 2010
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedical educationPsychologyFace-to-faceFace (sociological concept)Post hocPsychiatryClinical psychologyMedicine

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this paper was to determine the teaching activities most valued by medical students in the psychiatry rotation and to examine whether this varies with age, gender or stage of training. METHOD: Medical students at Sydney Medical School were surveyed following an 8-week clinical attachment in psychiatry. Differences in ratings between activities were examined by means of ANOVA and post hoc contrasts. RESULTS: The learning activities involving face-to-face teaching received the highest ratings. The most valued tutorial was that given weekly by a dedicated academic clinician. No differences were seen with respect to age and gender. Inconsistent differences were seen between stages of training for skills sessions, but no other learning activities. CONCLUSIONS: Face-to-face clinical teaching should be a priority in clinical attachments in psychiatry as it is highly valued by students and contributes to positive attitudes towards psychiatry.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.010
GPT teacher head0.358
Teacher spread0.348 · 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 source (direct Gemma or distilled Codex), 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

Citations22
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueAustralasian PsychiatrySame topicInnovations in Medical EducationFrench-language works237,207