Strategies to Overcome Therapeutic Inertia in Type 2 Diabetes Mellitus: A Scoping Review
Bibliographic record
Abstract
The objectives of this review were to: 1) examine recent strategies and component interventions used to overcome therapeutic inertia in type 2 diabetes mellitus (T2DM), 2) map strategies to the causes of therapeutic inertia they target and 3) identify causes of therapeutic inertia in T2DM that have not been targeted by recent strategies. A systematic search of the literature published from January 2014 to December 2019 was conducted to identify strategies targeting therapeutic inertia in T2DM, and key strategy characteristics were extracted and summarized. The search identified 46 articles, employing a total of 50 strategies aimed at overcoming therapeutic inertia. Strategies were composed of an average of 3.3 interventions (range, 1 to 10) aimed at an average of 3.6 causes (range, 1 to 9); most (78%) included a type of educational strategy. Most strategies targeted causes of inertia at the patient (38%) or health-care professional (26%) levels only and 8% targeted health-care-system-level causes, whereas 28% targeted causes at multiple levels. No strategies focused on patients' attitudes toward disease or lack of trust in health-care professionals; none addressed health-care professionals' concerns over costs or lack of information on side effects/fear of causing harm, or the lack of a health-care-system-level disease registry. Strategies to overcome therapeutic inertia in T2DM commonly employed multiple interventions, but novel strategies with interventions that simultaneously target multiple levels warrant further study. Although educational interventions are commonly used to address therapeutic inertia, future strategies may benefit from addressing a wider range of determinants of behaviour change to overcome therapeutic inertia.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".