Prescribing diabetes nutrition therapy: a qualitative study of dietitians’ experiences of carbohydrate restriction in Canada
Bibliographic record
Abstract
Background: Diabetes care has traditionally not included nutrition therapy using carbohydrate restriction, nor has carbohydrate restriction been taught to registered dietitians (RDs) to support patients living with diabetes choosing this dietary approach. We aimed to describe the experiences and views of RDs caring for patients using therapeutic carbohydrate-restricted diets (TCR), particularly metabolic conditions such as type 2 diabetes or prediabetes. Subjects/Methods: A qualitative study design using free-text responses from an online needs assessment survey was employed. RDs who practised in Canada were invited (n=6640) and 274 completed the survey, with 45 respondents who regularly prescribed TCR to their patients providing open-text responses (2987 words), which were analysed using inductive thematic analysis. Results: We identified four themes characterising Canadian RDs' experiences around prescribing TCR: interpersonal context, personal experience/knowledge, regulatory environment and patient-centredness. While these themes often interacted, each impacted TCR prescription uniquely, with patient-centred care at the core of reported experiences of prescribing. Conclusions: There exists a variety of experiences and perspectives related to prescribing of TCR among Canadian RDs caring for patients with diabetes, and all focus on the patient's needs, benefits and preferences. Prescribing TCR was often informed by the scientific literature yet also by RDs' experiential knowledge. Responses highlighted a desire for evidence-based educational materials and greater discussion within the diabetes nutrition community on this topic.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.021 | 0.013 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".