Advancing Gender-Equitable, Affirmative and Integrated Dentistry in India: Multizonal National Benchmarking of Oral Health Professionals’ Gender Sensitivity, Inclusiveness, and Preparedness Using the Novel OHP-GSIP © Tool
Bibliographic record
Abstract
Background: Gender-diverse populations in India, including transgender and non-binary individuals, experience systemic barriers to healthcare, with dentistry remaining particularly underexplored. Despite legislative protections, oral health professionals (OHPs) often lack the knowledge, sensitivity, and preparedness needed to provide inclusive care. This study aimed to benchmark gender sensitivity, inclusivity, and clinical preparedness of Indian OHPs using the novel Oral Healthcare Professional’s Gender Sensitivity, Inclusivity, and Preparedness (OHP–GSIP ©) tool. Methods: A descriptive cross-sectional survey was conducted among 3660 registered dental practitioners across six zones of India using probability proportional to size sampling. The prevalidated OHP–GSIP © scale assessed five domains: gender sensitivity, inclusive environments, diversity in practice, professional attitudes, and preparedness for transgender oral healthcare. Data were collected through a structured online questionnaire and analyzed with SPSS 17.0 using descriptive statistics, chi-square tests, correlation matrices, and multiple regression. Results: Participants demonstrated moderate LGBTQIA+ knowledge (mean = 6.52/10, SD = 1.78) and comfort in treating transgender patients (mean = 3.81/5, SD = 1.09). Structural inclusivity was limited: only 23.5% reported gender-neutral restrooms, and 17.5% used non-binary intake forms. Over 90% expressed willingness to employ or collaborate with transgender colleagues, though this did not significantly predict clinical comfort. Regression analysis showed inclusivity in practice (β = 0.38, p < 0.001), awareness of gender-affirming clinics (β = 0.29, p < 0.001), and LGBTQIA+ knowledge (β = 0.22, p < 0.001) as the strongest predictors of comfort in treating transgender patients, collectively explaining 41% of the variance. Conclusion: While Indian OHPs displayed generally supportive attitudes toward transgender individuals, substantial gaps persist in structural inclusivity, clinical preparedness, and knowledge. Bridging these gaps requires systemic reforms in dental education, policy, and practice environments. The OHP–GSIP © tool provides a benchmark for guiding curricular integration, institutional inclusivity, and policy advocacy toward equitable, gender-affirming oral healthcare.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| 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".