Not Just Checklists and Rainbows: Exploring Canadian Dietitians' Beliefs, Values, and Knowledge of Transgender Nutritional Care
Bibliographic record
Abstract
Purpose: Many transgender (short form: trans) people are experiencing disparities within Canadian health care systems, including nutritional and dietetic health care systems. This research explores the views, beliefs, and experiences of Canadian dietitians about trans nutritional care and seeks to understand how dietitians can better address the nutritional needs of trans individuals. Methods: Semistructured online interviews were conducted with 16 Canadian dietitians. Interviews were transcribed and the data were analyzed thematically. Results: Three main themes were created; (1) There's an Unjust System, (2) We've Come a Long Way, and (3) Not Just Checklists and Rainbows. The participants explored the historic nature of the Canadian dietetic profession and noted the connection between cis-normativity and the erasure of trans identities. They also explored how dietitians could better address the health needs of trans people, including moving beyond the acknowledgement of trans identities to changing the way gender is viewed in the profession. Conclusion: The dietetic profession must move beyond surface-level activities and rethink gender. Recommendations include adding trans-focused care training into the profession, creating safer spaces for trans individuals, advocacy and allyship, and recruiting trans people to the profession.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".