A longitudinal and multicentre study of burnout and error in Irish junior doctors
Bibliographic record
Abstract
BACKGROUND: Junior doctors have been found to suffer from high levels of burnout. AIMS: To measure burnout in a population of junior doctors in Ireland and identify if: levels of burnout are similar to US medical residents; there is a change in the pattern of burnout during the first year of postgraduate clinical practice; and burnout is associated with self-reported error. METHODS: The Maslach Burnout Inventory-Human Services Survey was distributed to Irish junior doctors from five training networks in the last quarter of 2015 when they were approximately 4 months into their first year of clinical practice (time 1), and again 6 months later (time 2). The survey assessed burnout and whether they had made a medical error that had 'played on (their) mind'. RESULTS: A total of 172 respondents out of 601 (28.6%) completed the questionnaire on both occasions. Irish junior doctors at time 2 were more burned out than a sample of US medical residents (72.6% and 60.3% burned out, respectively; p=0.001). There was a significant increase in emotional exhaustion from time 1 to time 2 (p=0.007). The association between burnout and error was significant at time 2 only (p=0.03). At time 2, of those respondents who were burned out, 81/122 (66.4%) reported making an error. A total of 22/46 (47.8%) of the junior doctors who were not burned out at time 2 reported an error. CONCLUSION: Current levels of burnout are unsustainable and place the health of both junior doctors and their patients at risk.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".