Collaborating with transgender youth to train healthcare professionals: randomized controlled trial of a didactic enhanced by brief videos
Bibliographic record
Abstract
Abstract BackgroundIn collaboration with members of the transgender and gender diverse (TGD) community, we created a didactic resource about the unique needs of TGD youth. MethodsWe developed teaching materials enhanced by video clips of two TGD adolescents openly sharing aspects of their lived experience. We compared the video and no video conditions in a randomized controlled trial (RCT) in which participants were assigned to one of four parallel conditions: 1) a transgender [TgV] or 2) a cisgender [CgV] woman presenting with videos embedded into the presentation, 3) the same cisgender woman presenting without the videos [CgN], or 4) a no intervention control [NiC]. Our primary outcome was change in the total score of the Transgender Knowledge, Attitudes, and Beliefs Scale (T-KAB). ResultsWe recruited and proportionally randomized 467 individuals, 200 of whom completed ratings before and after the intervention: TgV (n = 46), CgV (N = 46), CgN (n = 44), and NiC (n = 64). Mean scores on all measures of TGD acceptance increased in the video group, compared to the no video group. Improvements persisted after 30 days (p < 0.01), except on perceptions about TGD family members. The three active intervention groups did not differ in efficacy.ConclusionsThese findings provide empirical evidence that a well-informed presenter, regardless of their gender, can achieve similar improvements in perceptions and knowledge about TGD youth when using a resource that can be disseminated free of cost.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".