Males in Dietetics, What Can Be Learned from the Nursing Profession? A Narrative Review of the Literature
Bibliographic record
Abstract
In Canada 95% of dietitians are female despite serving a sex-diverse population. Literature examining why there are so few male dietitians is limited. However, nursing, like dietetics, is female dominated but has a large body of literature examining sex diversity within the profession. Therefore, a narrative literature review was conducted to find articles that examined the following questions: (i) What are the barriers and motivating factors for prospective male nursing students? and (ii) What are the perceived sex-based challenges that male nursing students encounter during their education? A total of 38 articles were included in the final review and the results are presented under the following headings: barriers, motivators, and educational experiences both in the classroom and during clinical rotations. The review outlines the current state of knowledge regarding sex as it relates to nursing and how this information compares with the current dietetics literature. Conclusions and recommendations are drawn about what changes could be made in dietetic education immediately and how further research could provide insight towards reducing the barriers and facilitating easier access to dietetics education for males.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".