The Ottawa resident observation form for nurses (O-RON): evaluation of an assessment tool’s psychometric properties in different specialties
Bibliographic record
Abstract
BACKGROUND: Workplace-based assessment (WBA) used in post-graduate medical education relies on physician supervisors' feedback. However, in a training environment where supervisors are unavailable to assess certain aspects of a resident's performance, nurses are well-positioned to do so. The Ottawa Resident Observation Form for Nurses (O-RON) was developed to capture nurses' assessment of trainee performance and results have demonstrated strong evidence for validity in Orthopedic Surgery. However, different clinical settings may impact a tool's performance. This project studied the use of the O-RON in three different specialties at the University of Ottawa. METHODS: O-RON forms were distributed on Internal Medicine, General Surgery, and Obstetrical wards at the University of Ottawa over nine months. Validity evidence related to quantitative data was collected. Exit interviews with nurse managers were performed and content was thematically analyzed. RESULTS: 179 O-RONs were completed on 30 residents. With four forms per resident, the ORON's reliability was 0.82. Global judgement response and frequency of concerns was correlated (r = 0.627, P < 0.001). CONCLUSIONS: Consistent with the original study, the findings demonstrated strong evidence for validity. However, the number of forms collected was less than expected. Exit interviews identified factors impacting form completion, which included clinical workloads and interprofessional dynamics.
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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 | 0.021 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".