Peer and Self-assessment of Professionalism in Undergraduate Medical Students at the University of Calgary
Bibliographic record
Abstract
Background: Peer and self assessment processes are integral to the development of professional behaviours. The purpose of this study was to assess the Rochester Peer Assessment Tool (RPAT) among a group of volunteer first year students.Methods: We assessed feasibility through participation rates. The evidence for the validity of instrument scores was ascertained through an exploratory factor analysis, MANOVA to determine age and gender differences, and a discrepancy analysis between the self and peer data. Reliability analyses included the Cronbach's alpha analysis and G- and D-studies. Students completed a feedback questionnaire to provide data about acceptability.Results: Self and peer data were collected for 46 and 44 students, respectively. Each student had a mean of 7.2 peer assessments (out of a possible 8). The factor analysis identified two factors, interpersonal skills and work study habits. The discrepancy analysis showed students in the lowest/highest quartiles, as assessed by peers, had higher/lower self means than peer means. The G-coefficient was Ep2 = 0.77. Student feedback was positive.Conclusions: RPAT was feasible in our setting, was acceptable to the students, and has been adopted as a mandatory part of our program for first and second year students. The study added to the evidence base for the reliability and validity of the RPAT instrument scores as a method of assessing professional behaviours.
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 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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".