Discrimination and Oral Health Impact: Moderating Role of Sex and Sexuality
Bibliographic record
Abstract
This study investigated whether sex assigned at birth and sexuality have a moderating role on the effect of discrimination on oral health impacts among adolescents. Using data from the Longitudinal Study of Australian Children, a sample representative of all Australian adolescents aged 14 to 15 y ( N = 2,905), we employed propensity score overlap weights to achieve covariate balance between participants exposed and unexposed to discrimination. Various forms of discrimination were examined: due to cultural background, due to mental health condition, due to sexual orientation, and due to sex assigned at birth. Oral health impact was assessed using the PedsQ Oral Health Scale. Poisson regression with robust variance was conducted including sex, sexual attraction, and discrimination as interaction terms. The sample included 1,407 females (49%) and 336 lesbian, gay, bisexual, and questioning (LGBQ) individuals (11%). More than 22% experienced at least 1 form of discrimination in the previous 6 mo. Findings from the overlap weighting analysis revealed that, in general, females had higher proportions of oral health impact compared with males, whereas sexually diverse youth tended to have worse oral health outcomes compared with non-LGBQ youth. A 2-fold higher prevalence rate of oral health impacts was found for sexually diverse females exposed to discrimination due to cultural background (95% confidence interval [CI]: 1.36–2.44) and due to mental health conditions (95% CI: 1.62–2.46). The largest effects of discrimination on oral health impacts were consistently observed among sexually diverse females. This novel study provides evidence on the moderating role of sex and sexuality in the relationship between discrimination and oral health among adolescents.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".