Knowledge, Attitudes, and Practices of Canadian Dietitians Regarding Supporting Individuals with Intellectual and Developmental Disabilities
Bibliographic record
Abstract
Purpose: To investigate the knowledge, attitudes, practices, and perceptions of Canadian dietitians regarding supporting individuals with intellectual and developmental disabilities (IDDs) in their practice. Methods: A 25-question cross-sectional online survey was developed by the research team, reviewed by experts, mounted on Research Electronic Data Capture (REDCap), and advertised to dietitians across Canada. Recruitment took place via email, social media, and e-newsletters. The survey was open in January–February 2024. Closed-ended responses were analyzed using descriptive statistics, and open-ended responses were analyzed using conventional content analysis. Results: In total, 136 respondents met inclusion criteria; 80% reported they have supported patients/clients with IDD in their role as a dietitian, and 96% agreed dietitians can positively influence the health outcomes of people with IDD. Over 50% of respondents did not feel they had access to appropriate client/caregiver resources, nearly 75% of respondents denied receiving dietetic training in the nutrition care of patients/clients with IDD. Most respondents (∼70%) were interested in learning more about supporting people with IDD as dietitians. Conclusions: Many respondents reported a gap in education, skills, and resources in this area. These findings can be used to improve dietetic training and inform future strategies to help reduce the shortcomings in health care experienced by people with IDD.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 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".