What is intersectionality and why is it important in oral health research?
Bibliographic record
Abstract
This paper is the second of two reviews that seek to stimulate debate on new and neglected avenues in oral health research. The first commissioned narrative review, "Inclusion oral health: Advancing a theoretical framework for policy, research and practice", published in February 2020, explored social exclusion, othering and intersectionality. In it, we argued that people who experience social exclusion face a "triple threat": they are separated from mainstream society, stigmatized by the dental profession, and severed from wider health and social care systems because of the disconnection between oral health and general health. We proposed a definition of inclusion oral health and a theoretical framework to advance the policy, research and practice agenda. This second review delves further into the concept of intersectionality, arguing that individuals who are socially excluded experience multiple forms of discrimination, stigma and disadvantage that reflect intersecting social identities. We first provide a theoretical and historical overview of intersectionality, rooted in Black feminist ideologies in the United States. Our working definition of intersectionality, requiring the simultaneous appreciation of multiple social identities, an examination of power and inequality, and a recognition of changing social contexts, then sets the scene for examining existing applications of intersectionality in oral health research. A critique of the sparse application of intersectionality in oral health research highlights missed opportunities and shortcomings related to paradigmatic and epistemological differences, a lack of robust theoretically engaged quantitative and mixed methods research, and a failure to sufficiently consider power from an intersectionality perspective. The final section proposes a framework to guide future oral health research that embraces an intersectionality agenda consisting of descriptive research to deepen our understanding of intersectionality, and transformative research to tackle social injustice and inequities through participatory research and co-production.
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 | 0.301 | 0.354 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.014 | 0.021 |
| Science and technology studies | 0.021 | 0.122 |
| Scholarly communication | 0.056 | 0.102 |
| Open science | 0.006 | 0.042 |
| Research integrity | 0.014 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 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".