Bibliographic record
Abstract
Purpose: A health knowledge translation comic book focused on body image for the GBTQ (gay, bisexual, trans, and queer) community was created. The project aimed to address the lack of nutrition-related health information for GBTQ men and to work towards improving their health and well-being by disrupting dominant body ideals. Summary of content: An anthology featuring 38 comic strips from various artists was produced. Comics focused on the social construction of bodies, fat stigma, masculinities, eating disorders, and the need for community support. Systematic approach: The work followed an arts-based methodological approach to create a comic anthology based on evidence-based literature and personal experiences from the GBTQ community. Comic book artists were recruited globally. Artists were asked to draw on their own personal experiences to create a comic strip regarding how society and culture influence body image and their health. One contributing artist was commissioned to illustrate nutritional health inserts for the comic based on a summary of peer-reviewed literature. The final book was printed and given to GBTQ health centres across Canada. Conclusions: An arts-based approach was an innovative approach to create an evidence-based knowledge translation comic that addresses nutrition-related health concerns for marginalized communities and to address the lack of representation for GBTQ men within a heteronormative dietetic profession. Recommendations: It is recommended that dietitians use arts-based approaches as knowledge translation strategies. The use of arts-based approaches can lead to a broader sense of what counts as knowledge and may influence the way nutritional health evidence is communicated and used within communities. Significance to the field of dietetics: Dietetic professionals are encouraged to consider the role of heteronormativity in dietetic practices and to challenge gender and sexual body norms that create nutrition-related health concerns.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.883 | 0.781 |
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".