Exploring Student Perspectives of the Dietetics Profession Using a Professional Socialization Lens
Bibliographic record
Abstract
Purpose: We aimed to explore student perspectives of the dietetics profession using a professional socialization lens. Methods: We conducted qualitative semi-structured interviews, virtually or by phone, with 25 dietetic undergraduate/graduate students or interns in 2020/21. Transcripts were thematically analyzed. Results: All participants identified as female, averaged 25 years old at the time of the interviews, and were in different stages of their education. Two themes captured their perspectives of the profession: dietitians have technical expertise and professional identities are evolving. Technical expertise was focused on scientific understandings of how individuals consume and utilize food, and how (mostly Western) food should be prepared for safety and maximum nutrition. Participants perceived dietetics as a white, feminized profession with dietitians’ role to aid in weight loss; participants actively sought to resist these stereotypes, notably through social media. Conclusions: While holding technical expertise continues to be embedded as a key component of dietetics identity, student professional socialization is also being shaped by social media, racial justice, and body positivity movements. This socialization process is likely to influence changes to the profession as students enter practice.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".