The social context of burning mouth syndrome: an exploratory pilot study of stigma, discrimination, and pain
Bibliographic record
Abstract
BACKGROUND: The social context of burning mouth syndrome (BMS) has received little attention in the scientific literature. However, social psychological theory and insights from those with lived experiences suggest that people living with BMS experience compounding effects of stigma related to their pain, diagnosis (or lack thereof), and intersectional identities. OBJECTIVE: Our aim is to provide initial evidence and to motivate new directions for research on BMS. Here, we present the results of an exploratory pilot study (n = 16) of women living with BMS in the United States. METHODS: Participants completed self-report measures of stigma, discrimination, and pain, as well as laboratory assessments of pain through quantitative sensory testing. RESULTS: Results indicate a high prevalence of internalized BMS stigma, experience of BMS-related discrimination from clinicians, and gender stigma consciousness in this population. Moreover, results provide initial evidence that these experiences are related to pain outcomes. The most robust pattern of findings is that internalized BMS stigma was related to greater clinical pain severity, interference, intensity, and unpleasantness. CONCLUSION: Given the prevalence and pain-relevance of intersectional stigma and discrimination identified in this pilot study, lived experience and social context should be incorporated into future research on BMS.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".