Understanding ethno-cultural differences in cardiac medication adherence behavior: a Canadian study
Bibliographic record
Abstract
Kathryn King-Shier,1,2 Hude Quan,2 Charles Mather,3 Elaine Chong,4 Pamela LeBlanc,1 Nadia Khan5 1Faculty of Nursing, University of Calgary, Calgary, Canada; 2Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Canada; 3Department of Anthropology, Faculty of Arts, University of Calgary, Calgary, Canada; 4Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, Canada; 5Division of General Internal Medicine, Faculty of Medicine, University of British Columbia, Vancouver, Canada Background: There are ethno-cultural differences in cardiac patients’ adherence to medications. It is unclear why this occurs. We thus aimed to generate an in-depth understanding about the decision-making process and potential ethno-cultural differences, of white, Chinese, and south Asian cardiac patients when making the decision to adhere to a medication regimen. Methods: A hierarchical descriptive decision-model was generated based on previous qualitative work, pilot tested, and revised to be more parsimonious. The final model was examined using a novel group of 286 cardiac patients, using their self-reported adherence as the reference. Thereafter, each node was examined to identify decision-making constructs that might be more applicable to white, Chinese or south Asian groups. Results: Non-adherent south Asians were most likely to identify a lack of receipt of detailed medication information, and less confidence and trust in the health care system and health care professionals. Both Chinese and south Asian participants were less likely to be adherent when they had doubts about western medicine (eg, the effects and safety of the medication). Being able to afford the cost of medications was associated with increased adherence. Being away from home reduced the likelihood of adherence in each group. The overall model had 67.1% concordance with the participants’ initial self-reported adherence, largely due to participants’ overreporting adherence. Conclusion: These identified elements of the decision-making process are generally not considered in traditionally used medication adherence questionnaires. Importantly these elements are modifiable and ought to be the focus of both interventions and measurement of medication adherence. Keywords: cardiac, descriptive decision-modeling, ethno-culture, medication adherence
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".