Selecting an intervention to prevent ketoacidosis at diabetes diagnosis in children using a behavior change framework
Bibliographic record
Abstract
OBJECTIVE: The rate of diabetic ketoacidosis (DKA), a preventable, life-threatening complication of diabetes, at the time of diagnosis in children remains unacceptably high worldwide. We describe our initial approach to selecting a national DKA prevention strategy, to be implemented by the Canadian Pediatric Endocrine Group DKA Prevention Working Group, informed by a framework for behavior change interventions. METHODS: Existing interventions were identified from a systematic review and our own gray literature search. We then characterized interventions using the Behavior Change Wheel, a framework to inform and drive behavior change, and matched interventions to behavioral targets, audiences, and identified barriers and facilitators. Feedback from the CPEG DKA prevention working group was incorporated into the intervention plan. RESULTS: We identified 27 interventions. Our proposed target behaviors are: (1) prompt recognition of symptoms of diabetes in children; (2) urgent attendance to medical care with a request for an office-based test for diabetes; and (3) rapid confirmation of diagnosis and urgent consultation with pediatric diabetes experts. We initially identified four possible intervention functions including education, training, environment restructuring, and enablement. Feedback from the working group favored education intervention functions including symptom recognition messages targeting parents, caregivers, teachers, and providers and messages about how to make a rapid diagnosis and need for urgent referral targeting providers. CONCLUSIONS: The Behavior Change Wheel has been used successfully in selecting interventions in other clinical areas. We describe how we used this framework to provide a foundation for developing an intervention to prevent DKA at diabetes diagnosis in children.
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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 | 0.027 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".