Development of the Whole Day Matters Toolkit for Primary Care: a consensus-building study to mobilize national public health guidelines in practice
Bibliographic record
Abstract
INTRODUCTION: Strategic knowledge mobilization efforts are needed to enhance uptake and use of the Canadian 24-Hour Movement Guidelines (24HMG), which describe optimal amounts of physical activity, sedentary behaviour and sleep each day for overall health. The Whole Day Matters Toolkit for Primary Care is an evidence-informed resource to help primary care providers (PCPs) disseminate the 24HMGs. The purpose of this study was to describe gaining consensus on toolkit components through iterative revisions to improve its utility in preparation for the September 2022 launch, and to summarize early dissemination efforts. METHODS: A multidisciplinary expert working group planned three modified Delphi surveys to assess PCPs' level of agreement with toolkit components on 7-point Likert scales with follow-up prompts for ratings of 4 or less. Consensus was defined a priori as a mean of 6 or higher out of 7 and 60% or more of PCPs selecting at least "somewhat agree." Items on which consensus was reached were removed from subsequent surveys unless they were revised. RESULTS: Twenty PCPs completed surveys 1 and 2; 15 completed survey 3. Consensus was reached on 5% (4/83), 17% (14/83) and 55% (38/69) of the items in surveys 1, 2 and 3, respectively. The number of qualitative comments decreased from 26 to 19 to 12, further indicating increasing consensus. CONCLUSION: Items on which consensus was not gained may reflect differences in provider characteristics or settings. A coproduced dissemination strategy was enacted. Toolkit reach was evaluated at launch and 4 months later.
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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 | 0.244 | 0.251 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| 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".