<i>Canadian Dietitians’ Use and Perceptions</i>Of Glycemic Index in Diabetes Management
Bibliographic record
Abstract
PURPOSE: Several health organizations, including the Canadian Diabetes Association, advocate use of the glycemic index (GI) in the nutritional management of diabetes. However, the clinical utility and applications of the GI remain controversial. Our goal was to determine, via a postal survey, whether dietitians were using the GI and barriers to its use if they were not. METHODS: This cross-sectional study was conducted in 2003. Members of Dietitians of Canada and the Ordre professionnel des diététistes du Québec (n=6,060) were first contacted by mail to identify those working with individuals with diabetes. Among respondents (n=2,857), 1,805 worked with individuals with diabetes and were sent a questionnaire. Using Chi-square analyses, users and nonusers were compared for their professional characteristics, perceived benefits, barriers, general knowledge about the concept, and confidence in teaching the GI. RESULTS: Among questionnaire respondents (n=1,057), 39% (n=415) used the GI and 61% (n=642) did not. Overall, users were more likely to have a greater diabetes patient caseload, perceived greater benefits and had greater confidence in teaching the concept. Nonusers cited lack of teaching tools and lack of knowledge on how to teach the concept as major barriers. CONCLUSIONS: Further research is required to identify the clinical reasoning that triggers dietitians to apply the concept in their 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".