Medication Self-Management for Home Care Users Receiving Multidose Drug Dispensing: Qualitative Interview Study
Bibliographic record
Abstract
Background: Multidose drug dispensing (MDD) is an adherence aid where medicines are machine-dispensed in disposable unit bags, usually for a 14-day period. MDD replaces manually filled dosettes in many home care services in Norway. While evidence suggests that MDD can improve medication adherence and reduce errors, there are few studies on how patients manage MDD at home and how this affects their daily routines. Objective: The aim of the study is to identify factors influencing medication self-management behavior among MDD users living at home and explore how MDD affects medication self-management. Methods: We conducted semistructured interviews with 19 MDD users in Oslo between August 2019 and February 2020. The interviews were held at the participants' homes, and the interview transcripts were analyzed thematically. Results: All participants in the study received some form of assistance with medication management from home care services. This assistance ranged from MDD delivery every other week to actual assistance with medication administration multiple times daily. However, regardless of the level of assistance received, participants primarily managed their MDD medications themselves. Daily medication routines and knowledge about medicines varied among the participants, with some taking an active role in their medication management, while others relied on others to take responsibility. The degree of involvement seemed determined by motivation rather than capability. Conclusions: MDD can support medication self-management, but its effectiveness varies among patients. The level of medication management by MDD users is not solely determined by their actual capabilities. Factors such as interest in self-care and independence, available support, information, and cognitive capacity all play a role in determining the degree of autonomy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".