Men who are Dietitians: Deconstructing Gender within the Profession to Inform Recruitment
Bibliographic record
Abstract
Purpose: In Canada, few men are dietitians. Literature is sparse regarding why so few men are drawn to dietetics. This study, part of a larger qualitative study, explores the experiences of men who are dietitians throughout their training and careers using a phenomenology framework. The study examines the meanings participants make about dietetics in relation to recruitment. Methods: Semi-structured individual interviews with 6 men who are dietitians were completed, transcribed, and analyzed. Results: An overarching theme, “experiences and outcomes of a gendered profession”, was related to the participants’ perspectives concerning recruitment into the dietetic profession. Four sub-themes are reported: (i) societal gender division, (ii) gender division within the profession, (iii) isolation from men who are mentors and other men, and (iv) the need to deconstruct and change. The results provide insight into recruitment barriers and potential approaches for increasing the number of men within dietetics, including changing the perceptions of the profession, increasing role models for men, and dismantling gendered practices. Conclusion: Participants believed that increasing men within dietetics would be beneficial and would increase diversity. It is unlikely that recruitment of men will increase if the status quo and gender norms of the profession are not disrupted and challenged.
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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 | 0.052 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.028 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.011 |
| 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".