Aiming for safer patient care: development of a needs-based anesthesia return-to-work program after a non-remedial leave of absence
Bibliographic record
Abstract
PURPOSE: During anesthesiologists' careers, a leave of absence (LOA) is common. After prolonged leave, updating may be beneficial in reducing concerns about knowledge and skill decrements. Although formal return-to-work (RTW) courses and checklists assist UK practitioners, and Australia mandates a one-month RTW program for each year away from practice, no Canadian RTW programs exist. This project aimed to determine the needs of anesthesiologists for an RTW program. METHODS: This quality improvement activity developed a needs analysis survey that was sent to all practicing anesthesiologists in Alberta. Respondents provided their opinions about the requirements necessary for an RTW program. RESULTS: Seventy-three of 350 eligible participants (21%) responded; one-third of respondents were female. Thirty-four respondents (47%) had taken at least one LOA, with a median [interquartile range] duration of 6 [3-12] months. Overall, respondents thought the duration of an LOA requiring formal RTW updating should be 12 [6-15] months, with a median updating period of 7 [5-20] days. Those who had previously taken an LOA thought updating should occur after a shorter absence (11 [6-12] vs 12 [6-24] months, P = 0.009) and be shorter (5 [3-12] vs 10 [5-26] days, P = 0.007). Comments indicated RTW updating should be flexible and individualized. Upgrades of computer systems and equipment plus specific skills retraining were identified. CONCLUSIONS: Leave of absences are common among anesthesiologists. Appropriate departmental support before, during, and after a gap in clinical practice could be provided by an RTW program to help endorse knowledge, skills, and confidence. Results identified the needs of Albertan anesthesiologists and provided initial guidance in the design of a user-centred RTW program.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".