Effect of a Narrative-Based Online Course Aimed at Reducing Stigma Toward Transgender Children and Adolescents: Longitudinal Observational Study
Bibliographic record
Abstract
BACKGROUND: Stigma toward transgender children and adolescents negatively impacts their health and educational outcomes. Contact with members of stigmatized groups can dismantle stereotypes and reduce stigma by facilitating exposure to the unique cognitive and emotional perspectives of individuals within the group. Recent evidence suggests that video-based contact interventions can be as effective as face-to-face encounters, but challenges lie in protecting the identities of transgender youth, since many of them live in stealth. OBJECTIVE: This study aims to evaluate the impact of an animated online course, rooted in authentic, personal narratives, on course participants' stigma toward transgender youth. METHODS: The online course was offered free of charge on Coursera and contained 19 teaching videos (3-7 minutes each), intermittent practice quizzes, and discussion prompts. Using real voice recordings of transgender children and their caregivers, the videos were designed to elicit empathy and transmit knowledge. All videos conveying the narratives of transgender youth were animated to protect their identities. A total of 447 course participants, distributed around the globe, completed pre- and postcourse surveys. While the course primarily targeted parents and caregivers of transgender youth, it was open to anyone with a Coursera account. The survey was based on the Transgender Attitudes and Beliefs Scale but modified to reflect the context of parents and caregivers. Using a 5-point Likert scale, it contained 5 questions that captured participants' levels of transgender stigma. Results of the pre- and postcourse surveys were then compared. RESULTS: The results were obtained in January 2023. Baseline levels of stigma were relatively low (18/25 across all questions, with 25 representing the lowest possible levels of stigma) and decreased further after completion of the course (to 19/25 across all questions, P<.001). A multivariate ordinal probit regression showed that, depending on the question, participants were 7%-34% more likely to endorse statements that indicated the lowest levels of stigma after completing the course. The course was equally effective across all demographics represented in our participant population. CONCLUSIONS: Our findings document a significant reduction in stigma toward transgender youth in participants who chose to enroll in the first animated, open online gender health course, rooted in the authentic narratives of transgender youth. Stigma levels decreased significantly after taking the course, even among participants whose baseline levels of stigma were low. Future interventions should include participants with more variable baseline levels of stigma, ideally in the setting of a randomized controlled trial. Despite its limitations, this evaluation adds to the existing evidence that digital, contact-based antistigma interventions, animated to protect the identity of the narrators, can effectively reduce stigma toward transgender youth.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".