“I would have preferred more options”: accounting for non‐binary youth in health research
Bibliographic record
Abstract
As a research team focused on vulnerable youth, we increasingly need to find ways to acknowledge non-binary genders in health research. Youth have become more vocal about expanding notions of gender beyond traditional categories of boy/man and girl/woman. Integrating non-binary identities into established research processes is a complex undertaking in a culture that often assumes gender is a binary variable. In this article, we present the challenges at every stage of the research process and questions we have asked ourselves to consider non-binary genders in our work. As researchers, how do we interrogate the assumptions that have made non-binary lives invisible? What challenges arise when attempting to transform research practices to incorporate non-binary genders? Why is it crucial that researchers consider these questions at each step of the research process? We draw on our own research experiences to highlight points of tensions and possibilities for change. Improving access to inclusive health-care for non-binary people, and non-binary youth in particular, is part of creating a more equitable healthcare system. We argue that increased and improved access to inclusive health-care can be supported by research that acknowledges and includes people of all genders.
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 | 0.205 | 0.251 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.030 |
| Scholarly communication | 0.013 | 0.028 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".