Formulating medication adherence strategies using the PASSAction framework
Bibliographic record
Abstract
Completing my final co-op placement at the Canadian Pharmacists Association (CPhA) not only helped me to integrate my prior pharmacy experiences in research, community and hospital practice but made me aware of an area of patient care that I am beginning to grow passionate about—medication adherence. Adherence, as defined by the World Health Organization, is the extent to which a person’s behaviour—taking medication, following a diet and/or executing lifestyle changes—corresponds with agreed-upon recommendations from a health care provider.1 Every year in Canada, patient nonadherence to drug therapy is estimated to cost the health care system $8–$10 billion and is associated with about 140,000 hospital admissions and 35,000 deaths.2 Yet adherence does not appear to be a clinical priority among community pharmacists.3 This phenomenon is compounded by the fact that pharmacists are currently not reimbursed for providing services specifically aimed to improve adherence. Additionally, family physicians perceive inadequate time and financial compensation as barriers that inhibit them from collaborating fully with community pharmacists in order to promote patient adherence.4 Despite a general apathy among health care providers around the problem of nonadherence,2 we should recognize that this is a complex, patient-specific and often multifactorial issue. No single intervention alone has consistently been shown to be effective at improving adherence. Interventions that have increased adherence, improved health outcomes and, in some cases, even reduced health care costs are more likely to use a combination of approaches individualized for the patient’s needs.5-7 In light of these issues, my aim in this commentary is to describe a logical framework that pharmacists can consider using in their daily practice to formulate and implement practical and individualized medication adherence strategies for their patients. This framework was inspired by my past co-op experiences and integrates key messages in some of the Canadian literature published on adherence to date. Abbreviated as PASSAction, this framework encompasses: P—Problem or Patient encounter A—Adherence factor or Align with the medication-taking process SS—Set a focused Strategy Action—put the strategy to Action To illustrate the use of PASSAction, this framework will be applied to 3 real-life cases of actual or potential nonadherence encountered in my co-op experiences. In order for adherence strategies to be effective and sustainable, the application of this framework will also convey the following key concepts: 1) individualize the strategy and adherence intervention, 2) be creative and 3) make use of existing resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".