Successful Health Care Provider Strategies to Overcome Psychological Insulin Resistance in United States and Canada
Bibliographic record
Abstract
PURPOSE: To identify specific actions and characteristics of health care providers (HCPs) in the United States and Canada that influenced patients with type 2 diabetes who were initially reluctant to begin insulin. METHODS: Patients from the United States (n = 120) and Canada (n = 74) were recruited via registry, announcements, and physician referrals to complete a 30-minute online survey based on interviews with patients and providers regarding specific HCP actions that contributed to the decision to begin insulin. RESULTS: The most helpful HCP actions were patient-centered approaches to improve patients' understanding of the injection process (ie, "My HCP walked me through the whole process of exactly how to take insulin" [helped moderately or a lot, United States: 79%; Canada: 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" [United States: 76%; Canada: 82%]). Actions that were the least helpful included referrals to other sources (ie, "HCP referred patient to a class to help learn more about insulin" [United States: 40%; Canada: 58%]). CONCLUSIONS: The study provides valuable insight that HCPs can use to help patients overcome psychological insulin resistance, which is a critical step in the design of effective intervention protocols.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| 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".