Bibliographic record
Abstract
Food is strongly symbolic and can hold either divergent or similar meanings to people from different backgrounds. The purpose of this research was to understand the roles food plays in the personal and professional lives of dietetics students and registered dietitians across Canada. This exploratory qualitative study included dietetic students (n=10) in Atlantic Canada and the Dietitians of Canada Student Network, and dietitians (n=15) from across Canada. The meaning of food was examined through the Photovoice technique with individual in-depth interviews to examine food perspectives and understandings. Transcripts were analyzed, ensuring data trustworthiness, through dependability, confirmability, and transferability of data and methods. Findings showed that shared food experiences might produce and reinforce emotional and social bonds among individuals. Sharing foods often helped to build social groups or communities. This study also identified the depth of contested relationships with food and negative language used to describe food, self, and food choice behaviours while underscoring the pressures to conform to a perceived external ideal. The results of this research highlighted the different roles and meanings that food plays for dietetics students and dietitians. These findings may initiate further self-reflection and empathy regarding food choice behaviours of self and others. Results might also clarify the impacts of personal epistemologies and ontologies on practice, which has the potential to strengthen the dietetics profession.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.138 | 0.033 |
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".