Training Dietitians to Champion LGBTQ+ Health
Bibliographic record
Abstract
Health inequalities experienced by LGBTQ+ people motivate a call for the queering of dietetics, through an embodiment of queer pedagogies in dietetic training. A critical review of LGBTQ+ inclusion strategies identified a paucity of focused pedagogic research, limiting the queering of dietetic training programs. This study appraised such teaching practices in the UK through a positive focus on “what works?”. An interview study was undertaken with 14 participants who were dietitians, dietetics lecturers, or current dietetics students, all identifying as LGBTQ+ people. Semi-structured interviews generated qualitative data on LGBTQ+ journeys through dietetic training and into practice. An appreciative inquiry approach was used to develop a solutions-focused template analysis to appraise innovative, imagined, and inspired practices in the queering of dietetic training in the UK. Thematic outputs were informed by appreciative inquiry’s positive modalities of change, illustrating diverse perspectives on the definition of cis-heteronormativity in dietetics, and the discovery, dreaming, design, and deployment of queering practices to support their disruption. The analysis generated complex multi-stakeholder actions to bring about the queering of dietetic training, and appraising current educational practices in universities, clinical placement, and other learning environments. The study revealed opportunities for the queering of dietetic training, exposing spaces of educational queering far beyond the classroom. For the queering of dietetic training, we present recommendations concerning the development of LGBTQ+ inclusive curricula, teaching practices, and wider professional strategy through the dietetics governing bodies. Further studies should expand appreciative exploration of the queering of dietetic training to allies and LGBTQ+ patients.
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".