Supporting Reflection on Medication Adherence: Eliminating a Blind Spot in Our Rearview Mirror
Bibliographic record
Abstract
Prescription medications are a vital form of medical intervention. About two thirds of adults in Canada and the U.S. have prescribed medications and statistics in comparable countries are similar. It is common for older adults or those with chronic conditions to take multiple different medications. The safety and effectiveness of medication-based therapies requires that patients adhere to their prescriptions. Improving adherence has been identified as a challenge (and opportunity) to increase health and wellbeing. Among the many factors that may impact adherence (including socio-economic status) are forgetfulness, unwanted side-effects, perceived lack of benefit, lifestyle conflicts, etc. Reflecting on past behaviours can help patients (and their care providers) to optimize adherence. Unfortunately, reflection is not well supported by current tools and technologies and relies on patient recollection and crude methods like pill counting. The CHAOS (Collaborative Health Adherence Optimization System) project seeks to address this “blind spot.” This paper reports on the requirements for reflection views elicited in the CHAOS project and a prototype design of a reflection tool.
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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 | 0.019 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".