How Do People With Dementia Make Sense of Their Medications? An Interpretative Phenomenological Analysis Study
Bibliographic record
Abstract
Abstract BACKGROUND: Managing medication is complex and multifaceted for people with dementia and their family carers. Despite efforts to support medication management, medication errors and medication-related hospital admissions still occur. This study investigated how people with dementia viewed and talked about their medications and medication-taking. METHODS: An interpretative phenomenological approach (IPA) qualitative research design combining photo elicitation and in-depth interviews was used. People with a diagnosis of mild or moderate dementia confirmed with Montreal Cognitive Assessment, took photographs of anything they viewed to be related to medication, with/out the help of family carers, over any two-day period. The photographs were then used as cues for a subsequent in-depth interview. Interview data were analysed using IPA.RESULTS: Twelve people with dementia were interviewed. In-depth analysis of interviews generated four themes: 1) Medication as a lifeline, 2) Overcoming the uncertainty about the effectiveness of donepezil, 3) Managing medications dominate daily lives and plans and 4) Sense of being and being in control. People with dementia view donepezil as a lifeline but some continually struggle to know whether it helps their condition. Despite this uncertainty, people with dementia continue to take their medications. Managing medications dominates their daily lives and plans and redefines them. CONCLUSIONS: This study provided unique insights into how people with dementia make sense of their medication. Healthcare professionals can use these insights to shape their practice around medication prescribing and advice. The findings are also useful to researchers looking to develop interventions to support medication management within the home setting.
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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".