Oral health‐related stigma: Describing and defining a ubiquitous phenomenon
Bibliographic record
Abstract
This paper is the fourth of a series of narrative reviews to critically rethink underexplored concepts in oral health research. The series commenced with an initial commissioned framework of Inclusion Oral Health, which spawned further exploration into the social forces that undergird social exclusion and othering. The second review challenged unidimensional interpretations of the causes of inequality by bringing intersectionality theory to oral health. The third exposed how language, specifically labels, can perpetuate and (re)produce vulnerability by eclipsing the agency and power of vulnerabilised populations. In this fourth review, we revisit othering, depicted in the concept of stigma. We specifically define and conceptualize oral health-related stigma, bringing together prior work on stigma to advance the robustness and utility of this theory for oral health research.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| 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".