Pillbox use, satisfaction, and effectiveness among persons with chronic health conditions
Bibliographic record
Abstract
The purpose of this study was to understand how persons with chronic health conditions use pillboxes, their satisfaction with current devices, and the impact of pillbox use on medication adherence. We used convergent parallel mixed methods approach to explore the experiences of 13 regular, 3 occasional, and 5 non-pillbox users. Medication consumers completed the Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST 2.0), an interview about their medication routines, and a medication adherence diary to describe their experiences with their pillboxes. Results demonstrated most participants use pillboxes to help manage their medications, and pillbox users tended to have better medication adherence than nonusers. Participants used a variety of pillboxes differing in size, shape, and color. Users reported selecting pillboxes based on their needs in addition to the demands of their habits and medication regimens. Users were generally satisfied with their pillboxes with an average QUEST score of 4.33. However, participants also identified areas for an improved design of pillboxes. Pillboxes can be an effective strategy to improve medication adherence. Improvements in device prescription, training, research, and design are needed to understand the mechanisms and size of effects of this intervention.
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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".