Risky alcohol use in Danish physicians: Associated with alexithymia and burnout?
Bibliographic record
Abstract
BACKGROUND: Alcohol abuse may be elicited by psychological problems and can influence physicians' health and patient safety. To act on it, we need knowledge on the prevalence of the disorder and its associations with psychological factors and physicians' well-being. The aim of this study was to explore whether burnout and alexithymia are associated with risky alcohol consumption in physicians and whether burnout mediates the association between alexithymia and risky alcohol consumption. METHODS: In this cross-sectional study, 4,000 randomly selected physicians received an electronic questionnaire by email containing the Alcohol Use Disorders Identification Test (AUDIT), the Maslach Burnout Inventory Human-Services-Survey (MBI-HSS) and the Toronto Alexithymia Scale (TAS-20). A total of 1,841 physicians completed the questionnaire (46%). RESULTS: 18.8% reached the criteria for risky alcohol consumption. The likelihood of having risky alcohol consumption was associated with high levels of alexithymia (OR=1.93, 95%CI=1.37-2.74, P<0.001). Moreover, risky alcohol consumption was associated with burnout (OR=1.86, 95%CI=1.13-3.05, P<0.014) and each individual burnout dimension: emotional exhaustion (OR=1.89, 95%CI=1.33-2.69, P<0.001), depersonalisation (OR=2.23, 95%CI=1.53-3.25, P<0.001) and low levels of personal accomplishment (OR=1.66, 95%CI=1.14-2.41, P=0.008). Mediation analysis suggested that the association between alexithymia and risky alcohol consumption was partially mediated through depersonalisation. CONCLUSIONS: The results emphasize a need for enhancing emotional self-awareness in physicians as psychological traits, work-pressure and alcohol dependence might be self-reinforcing aspects for the individual physician. As alcohol dependence and burnout may have consequences for patient safety separately, the aggregated influence of these factors has to be examined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".