Implementation of the CANRISK Tool: A Qualitative Exploration Among Allied Health Professionals in Canada
Bibliographic record
Abstract
OBJECTIVES: Launched in 2011 by the Public Health Agency of Canada, the Canadian Diabetes Risk Questionnaire (CANRISK) is a self-assessment tool validated in a Canadian sample, but its uptake has never been assessed. We sought to determine the level of current use of the CANRISK tool, identify common facilitators and barriers to its use and recommend future improvements. METHODS: Ten professional allied health organizations across Canada were contacted for in-depth interviews. Contextual content and thematic analysis were used to analyze the qualitative data set. RESULTS: According to allied health professionals, the tool is widely used, appealing and needed, and is being used for risk screening and health promotion. Respondents also identified the need to provide support and next steps for users identified as high risk. Still, several barriers to implementation were found, including readability, offensive or confusing language and difficulty ascertaining body measurements. CONCLUSIONS: The CANRISK is a valuable diabetes risk assessment tool in Canada, particularly for allied health organizations.
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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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 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".