Towards a sociological understanding of medical gaslighting in western health care
Bibliographic record
Abstract
In recent years, the term 'medical gaslighting' and accompanying accounts of self-identified women experiencing invalidation, dismissal and inadequate care have proliferated in the media. Gaslighting has primarily been conceptualized in the field of psychology as a phenomenon within interpersonal relationships. Following the work of Paige Sweet (American Sociological Review, 84, 2019, 851), I argue that a sociological explanation is necessary. Such an explanation illustrates how medical gaslighting is not simply an interpersonal exchange, but the result of deeply embedded and largely unchallenged ideologies underpinning health-care services. Through an intersectional feminist and Foucauldian analysis, I illuminate the ideological structures of western medicine that allow for medical gaslighting to be commonplace in the lives of women, transgender, intersex, queer and racialized individuals seeking health care. Importantly, these are not mutually exclusive groups, and I use the term bio-Others to highlight and connect how those with embodied differences are treated in medicine. This article indicates the importance of opening a robust discussion about the sociology of medical gaslighting, so that we might better understand what structural barriers people of marginalized social locations face in accessing quality health care and develop creative solutions to challenge health-care inequities.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.085 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| 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".