Burnout through the Lenses of Equity/Equality, Diversity and Inclusion and Disabled People: A Scoping Review
Bibliographic record
Abstract
Burnout is a problem within the workplace including in higher education, the activity of activism, and in reaction to experiencing systemic discrimination in daily life. Disabled people face problems in all of these areas and therefore are in danger of experiencing “disability burnout”/”disablism burnout”. Equity/equality, diversity, and inclusion” (EDI) linked actions are employed to improve the workplace, especially for marginalized groups including disabled people. How burnout is discussed and what burnout data is generated in the academic literature in relation to EDI and disabled people influences burnout policies, education, and research related to EDI and to disabled people. Therefore, we performed a scoping review study of academic abstracts employing SCOPUS, the 70 databases of EBSCO-HOST and Web of Science with the aim to obtain a better understanding of the academic coverage of burnout concerning disabled people and EDI. We found only 14 relevant abstracts when searching for 12 EDI phrases and five EDI policy frameworks. Within the 764 abstracts covering burnout and different disability terms, a biased coverage around disabled people was evident with disabled people being mostly mentioned as the cause of burnout experienced by others. Only 30 abstracts covered the burnout of disabled people, with eight using the term “autistic burnout”. Disabled activists’ burnout was not covered. No abstract contained the phrase “disability burnout”, but seven relevant hits were obtained using full-text searches of Google Scholar. Our findings suggest that important data is missing to guide evidence-based decision making around burnout and EDI and burnout of disabled people.
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 | 0.013 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.031 | 0.028 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".