Evaluation of off-service rotations at National Guard Health Affairs: Results from a perception survey of off-service residents
Bibliographic record
Abstract
CONTEXT: "Off-service" clinical rotations are part of the necessary requirements for many residency training programs. Because these rotations are off-service, little attention is given to their structure and quality of training. This often leads to suboptimal educational experience for the residents on these rotations. AIMS: The aim of this study was to assess medical residents' perceptions, opinions, and levels of satisfaction with their "off-service" rotations at a major residency training site in Saudi Arabia. It was also to evaluate the reliability and validity of a questionnaire used for quality assurance in these rotations. Improved reliability and validity of this questionnaire may help to improve the educational experience of residents in their "off-service" rotations. MATERIALS AND METHODS: A close-ended questionnaire was developed, Pilot tested and distributed to 110 off-service residents in training programs of different specializations at King Fahad Naitonal Guard Hospital and King Abdulziz Medical City, Riyadh, Saudi Arabia, between September 2011 and December 2011. RESULTS: A total of 80 out of 110 residents completed and returned the questionnaire. Only 33% of these residents had a clear set of goals and educational learning objectives before the beginning of their off-service rotations to direct their training. Surgical specializations had low satisfaction mean scores of 57.2 (11.9) compared to emergency medicine, which had 70.7 (16.2), P value (0.03). The reliability of the questionnaire was Cronbach's alpha 0.57. The factor analysis yielded a 4-factor solution (educational environment, educational balance, educational goals and objectives, and learning ability); thus, accounting for 51% variance in the data. CONCLUSION: Our data suggest that there were significant weaknesses in the curriculum for off-service clinical rotations in KAMC and that residents were not completely satisfied with their training.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".