Burnout among specialists and trainees in physical medicine and rehabilitation: A systematic review
Bibliographic record
Abstract
Professional burnout, emotional exhaustion and loss of satisfaction with patient care affects doctors at all stages of their career, from residency trainees to certified speci alists.Burnout is a critical emerging issue facing specia lists and trainees of all disciplines.Burnout in doctors is linked to serious negative outcomes for patients, inclu ding higher rates of medical errors and poorer quality of care.It is also linked to negative outcomes for doctors, including substance abuse and suicide.Although burn out is a serious problem, little is known about burnout in specialists and trainees in Physical Medicine & Rehabili tation (PM&R).Historically, it was thought that doctors in rehabilitation medicine were less likely to experience burnout than doctors in other specialties.A systematic review was conducted to understand if burnout is in fact a problem for doctors in PM&R.It was found that more than half of all rehabilitation doctors, including specia lists and trainees, experience burnout; a higher rate than for nonrehabilitation doctors.Working in PM&R is a unique risk factor for burnout among doctors.Important next steps will be to understand what causes such high rates of burnout and what can be done to help.Objective: Burnout, a state of emotional exhaustion related to work or patient-care activities, is prevalent in all stages of medical training and clinical practice.The syndrome has serious consequences, including medical errors, poorer quality of care, substance abuse, and suicide.The aim of this study is to evaluate the prevalence of burnout in Physical Medicine & Rehabilitation (PM&R) specialists and trainees.Methods: Systematic literature searches were conducted in MEDLINE, CINAHL and EMBASE for peerreviewed articles in English before March 2019 about the prevalence of burnout amongst PM&R specialists and trainees.Results: This systematic review yielded 359 results.Of these, 33 full-text records were reviewed; 5 met the inclusion criteria: 3 surveys of PM&R specialists and 2 of PM&R residents (total n = 1,886 physicians; year of publication 2012-2019).Data extracted included prevalence and severity of burnout and, if avail able, risk or protective factors.Data were analysed using descriptive statistics.Incidence of burnout ranged from 22.2% to 83.3% in trainees and 48% to 62% in specialists.Organizational and system challenges were the primary risk factors for burnout amongst specialists.Conclusion: Emerging evidence positions physicians in PM&R among the most likely to experience burnout.Although there is limited literature regarding PM&R specialists and trainees, the available evidence suggests that more than half of physicians in PM&R experience burnout.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".