Identifying solutions to psychological insulin resistance: An international study
Bibliographic record
Abstract
AIMS: To identify actions of healthcare professionals (HCPs) that facilitate the transition to insulin therapy (IT) in type 2 diabetes (T2D) adults. METHODS: Included were T2Ds in seven countries (n = 594) who reported initial IT reluctance but eventually began IT. An online survey included 38 possible HCP actions: T2Ds indicated which may have occurred and their helpfulness. Also reported were delays in IT start after initial recommendation and any period of IT discontinuation. RESULTS: Exploratory factor analysis of HCP actions yielded five factors: "Explained Insulin Benefits" (EIB), "Dispelled Insulin Myths" (DIM), "Demonstrated the Injection Process" (DIP), "Collaborative Style" (CS) and "Authoritarian Style" (AS). Highest levels of helpfulness occurred for DIP, EIB and CS; lowest for AS. Participants who rated DIP as helpful were less likely to delay IT than those who rated DIP as less helpful (OR = 0.75, p = 0.01); participants who rated CS and EIB as helpful were less likely to interrupt IT than those who rated these as less helpful (OR = 0.55, p < 0.01; OR = 0.51, p = 0.01, respectively). CONCLUSIONS: Three key HCP actions to facilitate IT initiation were identified as helpful and were associated with more successful initiation and persistence. These findings may aid the development of interventions to address reluctance to initiating IT.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".