Barriers and facilitators for optimizing oral anticoagulant management: Perspectives of patients, caregivers, and providers
Bibliographic record
Abstract
BACKGROUND: Oral anticoagulants (OACs) are very commonly prescribed for prevention of serious vascular events, but are also associated with serious medication-related bleeding. Mitigation of harm is believed to require high-quality OAC management. This study aimed to identify barriers and facilitators for optimal OAC management from the perspective of patients, caregivers and healthcare providers. METHODS: Using a qualitative descriptive study design, we conducted five focus groups, three with patients and caregivers and two with health care providers, in two health regions in Southwestern Ontario. An expert facilitator led the discussions using a semi-structured interview guide. Each session was digitally recorded, transcribed verbatim and anonymized. Transcripts were analyzed in duplicate using conventional content analysis. RESULTS: Forty-two (19 patients, 7 caregivers, and 16 providers including physicians, nurses and pharmacists) participated. More than half of the patients received OAC for the treatment of venous thromboembolism (57.9%) and the majority (94.7%) were on chronic therapy (defined as >3 years). Data analysis organized codes describing barriers and facilitators into 4 main themes-medication-related, patient-related, provider-related, and system-related. Barriers highlighted were problems with medication access due to cost, patient difficulties with adherence, knowledge and adjusting their lifestyles to OAC therapy, provider expertise, time for adequate communication amongst providers and their patients, and health care system inadequacies in supporting communications and monitoring. Facilitators identified generally addressed these barriers. CONCLUSIONS: Many barriers to optimal OAC management exist even in the era of DOACs, many of which are amenable to facilitators of improved care coordination, patient education, and adherence monitoring.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".