Measuring the impact of an HIV rotation on trainees’ knowledge and confidence level: The importance of inviting recent graduates
Bibliographic record
Abstract
Background: The new Canadian Residency Accreditation Consortium (CanRAC) standards recommend surveying recently graduated trainees to target improvements in training programs. The goal of this study was to estimate the impact of a rotation in an HIV clinic on trainees' related knowledge, confidence, and practice profile at the Université de Montréal. Methods: An electronic survey was sent to practising physicians who completed the rotation between 2006 and 2016. Participants were asked to rate their agreement and level of confidence toward HIV- and HCV-related topics using 5-point Likert scales (0 to 4). Descriptive statistics and mean comparisons were calculated. Results: Among invited participants, 27 of 45 (60%) completed the questionnaire. The majority of respondents were infectious diseases physicians (48%) or family physicians (37%) and had an outpatient caseload of <10 HIV patients/year (80%). For 37% of the respondents, the rotation had a large or very large impact on their career path. They considered that the rotation had increased their knowledge on the overall management of HIV (mean 3.2/4 [95% CI 2.9 to 3.4]), but less on pre-exposure prophylaxis (PrEP) (mean 1.5/4 [95% CI 1.1 to 2.0]) or HCV care (mean 1.9/4 [95% CI 1.4 to 2.3]). Participants felt less confident with genotyping interpretation (mean 2.6/4 [95% CI 2.2 to 2.9]) and PrEP (mean 2.4/4 [95% CI 2.0 to 2.8]). Conclusions: These results suggest that a rotation in an HIV clinic improves knowledge related to HIV care. Feedback from past graduates helped us identify gaps in knowledge or level of confidence in PrEP and HCV care, which will feed curriculum improvement.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".