Navigating an Imposed Diet: What Dietetic Students Learned From a Food Intervention Experience
Bibliographic record
Abstract
OBJECTIVE: To explore the experiences of female dietetic students strictly following 2 different isocaloric diets. DESIGN: A qualitative study as part of a randomized controlled feeding trial with a crossover design. Data were collected through individual interviews conducted 1-35 days after the intervention. SETTING: A university in Sweden. PARTICIPANTS: Normal-weight, healthy female dietetic students (n = 17), aged 18-30 years, who completed the full intervention. INTERVENTION: Two 4-week diet periods with a washout period in between. Participants followed either a diet based on the Nordic Nutrition Recommendations or a ketogenic low-carb high-fat diet, consuming preprepared meals. PHENOMENON OF INTEREST AND VARIABLES: Participants' experiences of following the imposed diets. ANALYSIS: Data were analyzed using content analysis techniques, resulting in 1 main theme, 2 categories, and 5 subcategories. RESULTS: Participants reported challenges with loss of control over food choices, unexpected effects of diet restrictions, and social visibility. They also highlighted the importance of contributing to science and the benefits for their future careers. CONCLUSIONS AND IMPLICATIONS: First-hand experiences of strict diets provided valuable insights into the complexities of dietary adherence, enhancing empathy and competence in future dietetic practice. Integrating practical diet experiences into education could improve dietary counseling skills.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".