Does clinical placement location affect medical student exam performance in psychiatry?
Bibliographic record
Abstract
Abstract of a poster presentation at the RANZCP 2015 Congress, Brisbane, Australia, 3-7 May 2015. Disciplines Medicine and Health Sciences | Social and Behavioral Sciences Publication Details Dawes, K., Lethbridge, A. & Pai, N. (2015). Does clinical placement location affect medical student exam performance in psychiatry?. Australian and New Zealand Journal of Psychiatry, 49 (Suppl. 1), 107-108. This journal article is available at Research Online: http://ro.uow.edu.au/smhpapers/2780 Poster Presentations RANZCP 2015 CONGRESs, Brisbane Convention and Exhibition Centre, 3–7 May 2015 Does Clinical Placement Location Affect Medical Student Exam Performance in Psychiatry? K Dawes, A Lethbridge, N Pai University of Wollongong, Wollongong, Australia Background: One of the many challenges in managing student clinical placements is trying to ensure equity of opportunity and experience in regards to meeting the curriculum objectives. Students often complain that they have been disadvantaged by their clinical placement due to variations in patient population and acuity, the availability of consultants, registrars and other health care staff to guide learning, and the presence of other students from all disciplines who compete for opportunities. Objectives: To identify if there is a relationship between psychiatry placement location in the Illawarra Shoalhaven Local Health District (ISLHD) and end of year psychiatry exam results for medical students from the University of Wollongong. Methods: We compared psychiatry oral and written exam results for six cohorts of students, from 2009 to 2014, across four different placement locations in the ISLHD (N = 450) using one-way multivariate analysis of variance. Findings: The multivariate effect of placement location was not significant (Pillai’s Trace = .013, F(6,892) = .994, p = .428). Univariate ANOVAs on the individual outcome variables were also non-significant (written exam scores, F(3, 446) = 1.373, p = .250; oral exam scores F(3,446) = .789, p = .501). Conclusions: Maintaining the quality and consistency of clinical placements will always be a challenge due to limited and varied opportunities, student numbers, and the dynamic nature of both the workforce and the patient populations. However, based on our findings, within our region there is no difference in placement location in regards to end of year psychiatry exam results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".