Burnout prevalence in medical and radiation oncologists at British Columbia Cancer Agency (BCCA).
Bibliographic record
Abstract
6071 Background: Burnout, reported to affect 30-60% of oncology workers, is a syndrome of psychological distress typically manifesting in three dimensions: Emotional Exhaustion (EE), Depersonalization (DP) and Low Personal Accomplishment (PA). Causal factors include workload, dealing with terminally ill patients and difficulties maintaining a balance between professional and personal life. As workload rises due to increased complexity of therapy and increasing prevalence of cancer patients, burnout may increase, especially in times of financial constraint. We sought to determine the prevalence of burnout in medical and radiation oncologists working at BCCA, which provides all radiation and the majority of medical oncology services to BC’s 4.5 million people. Methods: In March 2011, BCCA oncologists were invited to participate in a confidential online survey consisting of basic demographics and the 22 item MasLach Burnout Inventory (MBI) instrument, the latter a validated tool measuring distress in the three main dimensions of burnout. Normative data for physicians were used to interpret the results. Results: Response rate was 59%, female:male 40:60% with similar response rates for medical and radiation oncology (60 v 59%). Of the 73 who indicated their age range, 34 (47%) were between 35 and 44 years old. Respondents indicated that they had considered reducing their Full Time Equivalent (FTE) (67%) or leaving BC (46%). In those with at least 2 scores at a severe level, these rates were 76% and 71% respectively. Conclusions: Over 60% of responding BCCA oncologists report burnout in at least one domain of the MBI tool. Many have considered leaving the province or reducing their hours. These data are consistent with Grunfeld’s survey of Ontario oncologists (CMAJ 2000), although the rate of burnout is higher in this survey. Further research into ways to lessen burnout in oncology is urgently needed. [Table: see text]
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".