On Time—An Innovative Online Discussion Tool to Overcome Barriers to Insulin Initiation
Bibliographic record
Abstract
The On Time education program is an innovative, online point-of-care tool designed to uncover and address barriers to insulin initiation, and to facilitate timely insulin initiation, when appropriate, in insulin-naïve individuals with type 2 diabetes (T2D). A total of 195 health care professionals (HCPs) completed online profiles of 1025 insulin-naïve individuals with T2D currently treated with non-insulin antihyperglycemic agents (NIAHAs) and with a glycated hemoglobin (A1C) above the Diabetes Canada target (for most individuals ≤7%). After having completed the discussion tool and questionnaires, participating HCPs were asked to evaluate the program, tool, and questionnaires. Mean age of participants was 61.2 years; 55% were male; mean duration of diabetes was 10.5 years. For the majority of participants (70%) the recommended A1C target was ≤7.0%. On average, participants were prescribed 2.6 NIAHAs, mainly metformin and dipeptidyl peptidase-4 inhibitors. Prior to using the On Time discussion tool, only 23% of individuals with diabetes were judged as likely (16%) or extremely likely (7%) willing to initiate insulin. The leading barriers to initiating insulin were apprehension toward needles/injections (59%), belief that insulin was complicated (56%), and psychological insulin resistance (45%). After using the On Time discussion tool, participants’ perceived willingness to initiate insulin increased (likely: 34%, extremely likely: 28%). Initiation of insulin was planned in 77% of participating individuals. The evaluation questionnaire was completed by 149 HCPs (76.4%), and showed that the discussion tool was perceived to help HCPs address insulin-related barriers (82%), positively impacted their practice (79%), and improved their approach when discussing insulin initiation with individuals with diabetes (76%). Identifying the barriers to initiating insulin and providing educational interventions to address them may help to improve insulin acceptance. Disclosure J. Gilbert: Other Relationship; Self; AstraZeneca, Boehringer Ingelheim GmbH, Eli Lilly and Company, Merck & Co., Inc., Novo Nordisk Inc., Janssen Pharmaceuticals, Inc., Sanofi. G. MacNeill: Consultant; Self; Becton, Dickinson and Company. Advisory Panel; Self; Janssen Pharmaceuticals, Inc., Eli Lilly and Company, Novo Nordisk Inc.. Consultant; Self; Sanofi. E.M. Cooke: Other Relationship; Self; Abbott. Speaker's Bureau; Self; Becton, Dickinson and Company, LifeScan Canada. Other Relationship; Self; LifeScan Canada. Speaker's Bureau; Self; Merck & Co., Inc.. Advisory Panel; Self; Sanofi. Speaker's Bureau; Self; Sanofi. Other Relationship; Self; Sanofi, mdBriefCase Group.P. Filteau: None. M. Vallis: Speaker's Bureau; Self; Novo Nordisk A/S. Advisory Panel; Self; Novo Nordisk Inc.. Speaker's Bureau; Self; AbbVie Inc.. Advisory Panel; Self; Valeant Pharmaceuticals International, Inc.. Speaker's Bureau; Self; Merck & Co., Inc.. Advisory Panel; Self; Sanofi. Speaker's Bureau; Self; Sanofi. M. Groleau: Employee; Self; Sanofi. P. Javadi: Employee; Self; Sanofi. C. Lebovics: Employee; Self; Sanofi.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".