Understanding the burnout experience: recent research and its implications for psychiatry
Bibliographic record
Abstract
The experience of burnout has been the focus of much research during the past few decades. Measures have been developed, as have various theoretical models, and research studies from many countries have contributed to a better understanding of the causes and consequences of this occupationally-specific dysphoria. The majority of this work has focused on human service occupations, and particularly health care. Research on the burnout experience for psychiatrists mirrors much of the broader literature, in terms of both sources and outcomes of burnout. But it has also identified some of the unique stressors that mental health professionals face when they are dealing with especially difficult or violent clients. Current issues of particular relevance for psychiatry include the links between burnout and mental illness, the attempts to redefine burnout as simply exhaustion, and the relative dearth of evaluative research on potential interventions to treat and/or prevent burnout. Given that the treatment goal for burnout is usually to enable people to return to their job, and to be successful in their work, psychiatry could make an important contribution by identifying the treatment strategies that would be most effective in achieving that goal.
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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| 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".