Constructing “Burnout”: A Critical Discourse Analysis of Burnout in Postgraduate Medical Education
Bibliographic record
Abstract
PURPOSE: In 1974, Dr. Herbert Freudenberger coined the term burnout. With the creation of the Maslach Burnout Inventory in 1984, burnout went from a pop psychology term to a highly studied phenomenon in medicine. Exponential growth in studies of burnout culminated in its adoption into the International Classification of Diseases-11 in 2022. Yet, despite increased awareness and efforts aimed at addressing burnout in medicine, many surveys report burnout rates have increased among trainees. The authors aimed to identify different discourses that legitimate or function to mobilize burnout in postgraduate medical education (PGME), to answer the question: Why does burnout persist in PGME despite efforts to ameliorate it? METHOD: Using a Foucauldian discourse analysis, this study examined the socializing period of PGME as an entry point into burnout's persistence. The archive from which the discourses were constructed included over 500 academic articles, numerous policy documents, autobiographies, videos, documentaries, social media, materials from conferences, and threads in forums including Reddit. RESULTS: This study identified 3 discourses of burnout from 1974-2019: burnout as illness, burnout as occupational stress, and burnout as existentialism. Each discourse was associated with statements of truth, signs and signifiers, roles that individuals play within the discourse, and different institutions that gained visibility as a result of differing discourses. CONCLUSIONS: Burnout persists despite effort to ameliorate it because it is a productive construct for organizations. In its current form, it depoliticizes issues of health in favor of wellness and gives voice to the challenge of making meaning from the experience of being a clinician.
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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 | 0.022 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.012 | 0.021 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".