Exploring the Experiences and the Nutritional Supports of LGBTQ+ Canadians during the COVID-19 Pandemic
Bibliographic record
Abstract
Purpose: The COVID-19 pandemic has impacted the lives of lesbian, gay, bi, trans, queer, and other groups (LGBTQ+) within Canada. This research aimed to explore the experiences of LGBTQ+ Canadians in relation to their nutritional needs, practices, and supports during the COVID-19 pandemic. Methods: The qualitative study was framed within a poststructuralism and queer theory paradigm and consisted of an online questionnaire. Participants were recruited and asked to complete open-ended questions. Responses were coded using thematic analysis. Results: Seventy participants completed the questionnaire. Data analysis resulted in 3 major themes, including (i) (dis)comforts of food and eating, (ii) shifting views of food and food practices, and (iii) what supports? The themes revealed that many LGBTQ+ individuals experienced stress and anxiety during the COVID-19 pandemic. It was a time in which their views and practices of food, cooking, and eating were changed. Nutritional supports were discussed in terms of family, friends, and partners. Conclusion: The findings highlight the complexity to the meanings people give to food, cooking, and eating during stressful times. It is recommended that dietitians familiarize themselves with the experiences of LGBTQ+ people, especially during times of global health emergencies to ensure equitable health care for LGBTQ+ communities.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.009 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".