A Qualitative Study of Barriers to Medication-Taking Among People With Type 2 Diabetes Using the Theoretical Domains Framework
Bibliographic record
Abstract
Objective We aimed to better understand the challenges related to type 2 diabetes medication-taking through Theoretical Domains Framework (TDF)-guided interviews with people with type 2 diabetes with varying degrees of medication-taking. Methods One-on-one qualitative interviews following a semistructured discussion guide informed by the TDF were conducted. Thirty people with type 2 diabetes in Canada were interviewed, with representation from across the country, of both sexes (47% female), of people with various diabetes durations (mean 12.9 ± 7.9 years), with different types of medication plans (n = 15 on polypharmacy), and with various medication-taking levels (n = 10 each for low-, medium-, and high-engagement groups). Results Themes related to medication-taking from interviews mapped to 12 of the 14 TDF theme domains, with the exclusion of the knowledge and skills domains. The most prominent domains, as determined by high-frequency themes or themes for which people with low and high medication-taking had contrasting perspectives, were 1) emotion; 2) memory, attention, and decision processes; 3) behavioral regulation; 4) beliefs about consequences; 5) goals; and 6) environmental context and resources. Conclusion Through our interviews, several areas of focus emerged that may help efforts to increase medication-taking. To validate these findings, future quantitative research is warranted to help support people with type 2 diabetes in overcoming psychological and behavioral barriers to medication-taking.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".