Reducing Burnout and Promoting Health and Wellness Among Medical Students, Residents, and Physicians in Alberta: Protocol for a Cross-Sectional Questionnaire Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Burnout is an increasingly common and insidious phenomenon experienced by workers in many different fields, although it is of particular concern among physicians and trainees due to the nature of their work. It is estimated that one-third of practicing physicians will experience burnout during their career, and this rate is expected to continue to increase. Burnout has significant implications, as it has been identified as a contributor to increased medical errors, decreased patient satisfaction, substance use, workforce attrition, and suicide. OBJECTIVE This study will evaluate the prevalence and impact of burnout on physicians, residents, and medical students in Alberta. METHODS Quantitative and qualitative data collected through self-administered, anonymous, online questionnaires will be used in this cross-sectional provincial study design. Data collection tools were developed based on published literature and questions from previously validated instruments. The tools capture relevant demographic information, mental health status, and rates of burnout, as well as factors contributing to both burnout and resilience among respondents. We anticipate a sample size of 777 medical students, 959 residents, and 1961 physicians to represent the respective ratios of trainees and practicing physicians in the province of Alberta. RESULTS Study recruitment will begin in September 2020, with 4 weeks of data collection. The results of this study are anticipated within 12 months from the end of data collection. It is expected that the results will provide an overview of the prevalence of burnout among those training and working in medicine in Alberta, identify contributors to burnout, and help develop interventions aimed at reducing burnout. CONCLUSIONS This study’s aim is to examine burnout prevalence and contributing factors among medical trainees and physicians in Alberta. It is expected that the results will identify and examine individual and organizational practices that contribute to burnout and help develop strategies and interventions focused on mitigating burnout and its sequelae. INTERNATIONAL REGISTERED REPORT PRR1-10.2196/16285
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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 | 0.025 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 0.005 |
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".