Experiences of LGBTQ-identifying students, interns and practitioners of dietetics
Bibliographic record
Abstract
Individuals who identify as lesbian, gay, bisexual, transgender and/or queer (LGBTQ) currently comprise over five percent of the population in America. It is unknown how many dietetics students, interns, or practitioners identify as LGBTQ as these data are not collected by the Academy of Nutrition and Dietetics nor its “sister” organizations. Research suggests that LGBTQ-identifying folx, experience higher rates of anxiety, depression, stigmatization, discrimination and isolation and many have unmet healthcare needs. This holds for LGBTQ-identified individuals studying and practicing in healthcare professions but to date there have been no studies of the dietetics profession. To explore the experiences of LGBTQ-identifying dietetics students, interns and practitioners we conducted a mixed-methods study grounded in Feminist Standpoint theory. National convenience and snowball sampling strategies generated n=131 students, interns and professionals of ACEND-accredited programs for our survey and n=10 for our semi-structured interviews. Qualitative analyses identified themes of overt and covert “underminers” of success including heteronormative assumptions/talk, misgendering, microaggressions, self-editing and closeting, and identified a lack of adequate LGBTQ-related healthcare content in academics/training. Supporters of success included people/groups with supportive characteristics, therapists/mental health professionals, and passion for the field. Survey data indicated significant differences between heterosexual and non-heterosexual respondents to a number of questions including homosexuality in society, awareness of LGBTQ community experiences, importance of academic content/training on nutrition assessment/care of LGBTQ-identified folx and responsibility for advocacy for LGBTQ-identifying patients/clients. Our results indicate need for better data collection, improved coursework/training on inclusion and greater content on nutrition/healthcare needs for LGBTQ-identifying patients and clients.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".