615-P: Identification of Barriers to Medication Adherence in People with Type 2 Diabetes Using Qualitative Interviews and the Theoretical Domains Framework
Bibliographic record
Abstract
Despite the availability of many effective medications and management options, many people with type 2 diabetes have low adherence to treatments which can lead to adverse health outcomes. We aimed to better understand the challenges related to type 2 diabetes medication adherence through interviews with people with type 2 diabetes with varying degrees of medication adherence. Phone interviews were conducted in English or French, lasted 45 minutes, and followed a semi-structured discussion guide informed by the Theoretical Domains Framework (TDF) . Eligibility criteria included being diagnosed with type 2 diabetes for at least 2 years and current use of diabetes medication (s) . A total of 30 people with type 2 diabetes were interviewed, with representation across Canada, by gender (14F/16M) , years since diagnosis (12.9±7.9) , types of medication and regimen (n=15 on polypharmacy) , and medication adherence levels (n=for each of the low/medium/high adherence groups) . Themes related to medication adherence identified from interviews mapped to 12 of the 14 TDF domains, with the Knowledge and Skills domain being the exceptions. Compared to people with type 2 diabetes categorized to the high adherence group, those categorized to the low adherence group doubted the appropriateness of their medications; reported less access to healthcare providers; tended not to use organizational tools to help with taking medication; and discussed financial barriers to accessing their medications. In contrast to those in the low adherence category, we found that highly adherent people with type 2 diabetes often viewed taking their medication as an emotionally neutral task. To validate these findings, additional quantitative research is underway to help support people with type 2 diabetes to overcome the psychological and tangible barriers to adherence and impact the perception of taking medication as a task without emotional connotations and stigma. Disclosure M. Vallis: Advisory Panel; Bausch Health, Canada, Novo Nordisk Canada Inc. Consultant; Abbott Diabetes, LifeScan. Speaker's Bureau; AbbVie Inc., Bausch Health, Canada, LifeScan, Novo Nordisk, Novo Nordisk A/S. S. Jin: Advisory Panel; Novo Nordisk Canada Inc. Consultant; Abbott Diabetes, Boehringer Ingelheim International GmbH, Dexcom, Inc., HLS Therapeutics Inc., Janssen Pharmaceuticals, Inc., MDBriefcase. Research Support; Novo Nordisk Canada Inc. Speaker's Bureau; Abbott Diabetes, Boehringer Ingelheim International GmbH, Eisai Inc., EOCI Pharmacomm Ltd., Janssen Pharmaceuticals, Inc., Novo Nordisk Canada Inc., Pfizer Inc., Roche Diabetes Care. Other Relationship; Diabetes Canada. A. Klimek-Abercrombie: Employee; Novo Nordisk Canada Inc. G. Ng: Other Relationship; Novo Nordisk Canada Inc. A. Bunko: Other Relationship; Novo Nordisk Canada Inc. A.A. Kukaswadia: Other Relationship; Novo Nordisk. C.S. Neish: Other Relationship; Novo Nordisk Canada Inc.
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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 | 0.022 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".