(101) CULTURALLY TAILORED SEXUAL HEALTH EDUCATION: EVALUATING A PODCAST’S REACH AMONG SOUTH ASIAN WOMEN
Bibliographic record
Abstract
Abstract Introduction This podcast, produced by South Asian (SA) women and aimed at SA audiences, features licensed SA healthcare professionals from medicine, public health, and psychology. Each episode addresses health disparities affecting the SA population, with a focus on women but relevance across genders. Unique among SA-focused podcasts, it dedicates seven episodes to sexual and reproductive health, addressing a significant gap in culturally relevant content for SA communities in Western contexts. With around 4.4 million South Asians in the U.S., primarily in California, New Jersey, Texas, and Illinois, this demographic remains underserved in accessible, culturally tailored sexual health resources. Topics like sexual health are heavily stigmatized in traditional healthcare settings, making platforms like podcasts vital for accessible and relatable information. Objective This study examines listener demographics and engagement for the seven sexual health episodes to assess demand for culturally tailored health education and identify ways to enhance accessibility and reach. Methods Data from the seven sexual health episodes (from a total of 61) were collected from the podcast’s streaming platform, showing 504 streams for sexual health content out of 4801 plays across 60 countries. These episodes cover cultural stigmas surrounding sexual education, pelvic health, STI and pregnancy prevention (including LGBTQIA+ inclusivity), trauma recovery, and menstrual care. Listener demographics were divided by age and gender, with English as the primary language. Results The findings revealed significant demand for culturally specific sexual health content. Sexual Education for South Asians ft. Bushra Mollick ranks as the fifth most-played episode with 135 streams, while Unlocking the Secrets of Pelvic Health with Dr. Chauhan ranks eighth at 117 plays. Additionally, Know Your Rights, Period ties for eighth place, also with 117 plays. These statistics demonstrate that sexual health topics are among the most engaging for listeners, further confirming the need for accessible, culturally relevant information in this area. Demographics demonstrate a predominantly female audience, particularly for episodes addressing trauma and stigma-free care, which had 100% female engagement. The episode on LGBTQIA+ safe practices saw a broader audience with up to 25% male listeners, showing wider appeal. The most engaged age groups were 23-27 (33% of listeners) and 28-34 (30%), reflecting strong interest from young and middle-aged adults. Episodes on pelvic health and sexual trauma also resonated with the 35-44 age group, highlighting relevance for mid-life health concerns. Geographically, most listeners are U.S.-based (64-100% per episode), with significant international audiences in the U.K. (21%) and Canada (24%), and consistent engagement from India (4%). Conclusions This analysis underscores the potential of digital platforms to deliver culturally tailored medical education. The high engagement from young and middle-aged SA adults suggests a need for culturally sensitive resources outside traditional healthcare settings. Featuring SA healthcare professionals builds credibility, trust, and relatability, fostering open discussions on sexual health. Findings suggest that incorporating culturally informed digital content into medical curricula and healthcare training could enhance the accessibility and effectiveness of sexual health education for underserved communities. Disclosure No.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".