Patients’ views and experiences on the use and safety of directly acting oral anticoagulants: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Direct oral anticoagulants (DOACs) are considered high-risk medications and used to prevent thromboembolic events and stroke. This study aimed to examine patients' views and experiences of DOACs use and factors that can promote safety associated with DOACs. METHODS: In-depth interviews were conducted with adult patients who had been prescribed DOACs, identified and invited by local collaborators in three different tertiary care hospitals in Saudi Arabia. A topic guide developed based on was used to inform the interview. Data were analysed thematically. RESULTS: Data saturation was achieved by the ninth participants. Three major themes were identified: (1) factors affecting DOAC's safety from the patients view; (2) barriers to adherence to DOACs and (3) strategies to promote the safety of DOACs. Lack of knowledge of DOACs, using inappropriate sources of information, lack of communication with HCPs, difficulty in having access to DOACs and lack of monitoring were the main factors affecting the safe use of DOACs. Unavailability of the drugs and difficulty in timely getting to hospitals affected adherence. Patients acknowledged difficulties communicating with healthcare professionals, timely access to anticoagulation clinics and in obtaining their DOACs on time. CONCLUSIONS: There is a need to develop and evaluate theory-based interventions to promote patient knowledge, understanding and shared decision-making to optimise DOACs use and improve their safety.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".