Assessment of a peer review process among interns at an Australian hospital
Bibliographic record
Abstract
Purpose. This study considered how a peer review process could work in an Australian public hospital setting. Method. Up to 229 medical personnel completed an online performance assessment of 52 Junior Medical Officers (JMOs) during the last quarter of 2008. Results. Results indicated that the registrar was the most suitable person to assess interns, although other professionals, including interns themselves, were identified as capable of playing a role in a more holistic appraisal system. Significant sex differences were also found, which may be worthy of further study. Also, the affirmative rather than the formative aspect of the assessment results suggested that the criteria and questions posed in peer review be re-examined. Conclusion. A peer review process was able to be readily implemented in a large institution, and respondents were positive towards peer review generally as a valuable tool in the development of junior medical staff. What is known about the topic? The literature generally concurs that peer review is a useful tool in professional development and can provide a rounded view from diverse sources about a peer’s professional performance. It has been implemented in at least one Canadian medical facility as a mandatory process. What does this paper add? Our study identifies who is considered the most suitable peer(s) to assess interns, various substantive issues about peer review and about the process itself, and raises questions about the voluntary v. mandatory nature of peer review. It is the first study to trial peer review amongst interns in an Australian hospital. What are the implications for practitioners? That peer review is a suitable tool in professional development and generally supported in our study, suggesting that it could be implemented into Australian healthcare practice. However, education about the nature and value of peer review would be required amongst healthcare professionals, and the use of peer review could imply greater managerial engagement in medical practice. Peer review is a more effective assessment tool than that currently employed in many Australian hospitals.
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 | 0.067 | 0.187 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".