Successful Health Care Provider Strategies to Overcome Psychological Insulin Resistance in U.S. and Canada
Bibliographic record
Abstract
About 30% of patients with type 2 diabetes (T2D) are reluctant to initiate basal insulin when recommended. In the EMOTION study, we surveyed T2D adults from seven countries, who initiated basal insulin ≤36 months ago, had T2D for ≥12 months before basal insulin initiation, and were initially reluctant to begin insulin but were using insulin at the time of the survey, to identify key healthcare providers’ (HCPs) actions that influenced their decision to start insulin. Patients completed a 30-minute online survey that included a newly-designed 38-item questionnaire (Psychological Insulin Resistance [PIR] Action Survey; developed from 58 patient and HCP interviews in 6 of the 7 countries) aimed at identifying occurrence and helpfulness of HCP actions for the decision to begin basal insulin. Here we describe separately findings from 120 patients in U.S. and 74 patients in Canada. The most helpful HCP actions, conditional on occurring, were patient-centric approaches to improve understanding of the injection process ("My HCP walked me through the whole process of exactly how to take insulin" [helped moderately or a lot-U.S.: 79%; CAN: 83%]) and alleviate concerns ("My HCP encouraged me to contact his/her office immediately if I ran into any problems or had questions after starting insulin" [U.S.: 76%; CAN: 82%]). Items reported to be the least helpful, conditional on occurrence, included authoritarian statements and referrals to other sources. In U.S., the least helpful HCP action was: "My HCP warned me that he/she could not be responsible for what might happen if I did not start insulin soon" (43% helped moderately or a lot). In Canada, the least helpful action was: "My HCP helped me meet other people who had already been taking insulin for a while" (50%). The study provides the first evidence pointing to successful strategies for overcoming PIR, and is a critical step towards the design of effective intervention protocols for HCPs. Disclosure T.S. Tang: Consultant; Self; Eli Lilly and Company. D.M. Hessler: Consultant; Self; Eli Lilly and Company. W. Polonsky: Consultant; Self; Abbott, AstraZeneca, Dexcom, Inc., Sanofi, Novo Nordisk Inc., Eli Lilly and Company, Intarcia Therapeutics, Inc., Servier, Ascensia Diabetes Care, Merck & Co., Inc., MannKind Corporation, Glooko, Inc., Roche Diabetes Care Health and Digital Solutions. L. Fisher: Consultant; Self; Eli Lilly and Company, Abbott, Merck & Co., Inc. I. Hadjiyianni: Employee; Self; Eli Lilly and Company. Stock/Shareholder; Self; Eli Lilly and Company. D. Cao: Employee; Self; Eli Lilly and Company. Stock/Shareholder; Self; Eli Lilly and Company. B. Reed: None. S. Kalirai: Employee; Self; Eli Lilly and Company. Stock/Shareholder; Self; Eli Lilly and Company. T. Irani: Employee; Self; Eli Lilly and Company. J.I. Ivanova: Other Relationship; Self; Eli Lilly and Company. Employee; Spouse/Partner; Boehringer Ingelheim Pharmaceuticals, Inc. U. Desai: Other Relationship; Self; Eli Lilly and Company, AstraZeneca. M. Perez-Nieves: Employee; Self; Eli Lilly and Company. Stock/Shareholder; Self; Eli Lilly and Company.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".