Care-related regret in the intensive care unit and its association with burnout and intention to change profession: A survey study
Bibliographic record
Abstract
BACKGROUND: Intensive care unit (ICU) healthcare workers (HCWs) face burnout and retention challenges. We hypothesized that care-related regret may contribute. This study aimed to investigate the relationship between care-related regret, burnout, and turnover intention among ICU HCWs, and to identify associated risk and protective factors. METHODS: A survey was sent to 360 HCWs in four ICUs to measure regret intensity (Regret Intensity Scale-10), coping strategies (Regret Coping Scale), burnout (Copenhagen Burnout Inventory), and turnover intention. Analyses were stratified by profession, and regression models examined associations between regret, burnout, and turnover intention. RESULTS: A total of 158 HCWs (92 nurses; 66 physicians; 62 % female) participated. Nurses and physicians reported similar regret events in the last month (median: 3[IQR: 2-5]). Nurses presented higher regret intensity than physicians (32[27-36] vs. 27 [23-33], p < 0.01). The main cause of regret was futility of care (43 % of respondents). Nurses had higher burnout scores than physicians: personal- (67[54-83] vs. 54[38-63]), work- (64[48-79] vs. 46[25-57]), and patient-related (50[33-64] vs. 33[17-46]) burnout (p < 0.01) and considered quitting more frequently (45 % vs. 11 %, p < 0.01). In multivariable analyses, regret intensity was associated with all burnout subdomains. Maladaptive regret coping strategies were associated with both personal- and patient-related burnout. Perceived futility of care was associated with patient-related burnout. Regret intensity tended to be associated with turnover intention, but this was non-significant. CONCLUSIONS: Regret among ICU HCWs is related to futility of care and associated with burnout. Identifying regret may be a valuable strategy to mitigate burnout and improve retention in ICU settings.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".