“<i>This is killing me. Please let me leave</i>”: Trans and non-binary youth and sexual health education in Alberta
Bibliographic record
Abstract
This article highlights sexual health education findings from a larger qualitative research study that examines the lived experiences of transgender and non-binary (TNB) youth (aged 14–25) in Alberta. This study contributes to a growing field focusing on the unique experiences of TNB students in Canada by exploring the gaps and successes in sexual health education. Given that there is no federally mandated sexual health curriculum, and that each Canadian province and territory updates curriculum and teaches sexual health differently, this study aims to provide insights into the experiences of TNB youth and the sexual health education they have received in their formal educational environments. The research findings highlight two major themes and two sub-themes: (1) All participants found sexual health education in schools to be inadequate in some way. (2) If you want something done right, do it yourself. Sub-themes included; (2a) the community or internet media are key sources of relevant sexual health information, and (2b) youth have limited access to the sexual orientation and gender identity–specific sexual health information they want.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".