552-P: Barriers and Enablers to Diabetic Ketoacidosis (DKA) Prevention in Adults with Type 1 Diabetes—An Implementation Science Study
Bibliographic record
Abstract
Introduction & Objective: DKA prevention depends strongly on patient knowledge and self-management skills, but educational tools are inconsistent and complex. To co-create a new tool with people living with T1D, we first aimed to determine key barriers and enablers to DKA prevention through identification of the perspectives of people living with T1D, their caregivers, and healthcare providers. Methods: We conducted a qualitative study involving three independent focus groups. The focus group design and analysis were informed by the Action, Actor, Context, Target, and Time Framework (AACTT) to carefully define the key targeted behaviours for change and the Theoretical Domains Framework (TDF) to understand their determinants. The targeted behaviours related to testing ketone levels, acting upon ketone testing results, and seeking emergency medical care. Deductive coding and thematic analysis were used to categorize and describe barriers and enablers for each targeted behaviour. Results: A total of 9 people living with T1D, 1 caregiver, and 12 healthcare providers participated in our focus groups. Five key themes relating to six TDF domains emerged that influenced one’s ability to engage in the targeted behaviours. Key barriers included: 1) a fundamental lack of understanding of the clinical relevance of ketones and DKA (knowledge, beliefs about consequences); 2) negative experiences with the healthcare system and lack of access to supplies (environmental context and resources); 3) inability to retain ketone knowledge among numerous self-management burdens (memory). Key enablers included: 1) reminders from physicians and/or technology (reinforcement); 2) community supports and accessible resources (social influences). Conclusion: These key barriers and enablers to DKA prevention will inform the development of an educational tool co-created with people living with T1D designed to more effectively prevent DKA than do existing resources. Disclosure N. Verhoeff: None. W. Cheema: None. S. Mojdehi: None. H.Y. Gad: Consultant; Procter & Gamble. D. Mumford: None. A. Orszag: None. N. Ivers: Speaker's Bureau; Novo Nordisk. Consultant; Merck & Co., Inc. D.R. Budhram: None. A.M.K. Bakhsh: None. M.I. Abuabat: None. A. Weisman: None. B.A. Perkins: Advisory Panel; Abbott. Other Relationship; Novo Nordisk. Advisory Panel; Insulet Corporation, Nephris. Other Relationship; Medtronic. Advisory Panel; Sanofi, Vertex Pharmaceuticals Incorporated, Dexcom, Inc. Funding Diabetes Canada (Operating Grant OG-3-21-5572-BP)
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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 | 0.025 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".