Seniors managing multiple medications: using mixed methods to view the home care safety lens
Bibliographic record
Abstract
Abstract Background Patient safety is a national and international priority with medication safety earmarked as both a prevalent and high-risk area of concern. To date, medication safety research has focused overwhelmingly on institutional based care provided by paid healthcare professionals, which often has little applicability to the home care setting. This critical gap in our current understanding of medication safety in the home care sector is particularly evident with the elderly who often manage more than one chronic illness and a complex palette of medications, along with other care needs. This study addresses the medication management issues faced by seniors with chronic illnesses, their family, caregivers, and paid providers within Canadian publicly funded home care programs in Alberta (AB), Ontario (ON), Quebec (QC) and Nova Scotia (NS). Methods Informed by a socio-ecological perspective, this study utilized Interpretive Description (ID) methodology and participatory photographic methods to capture and analyze a range of visual and textual data. Three successive phases of data collection and analysis were conducted in a concurrent, iterative fashion in eight urban and/or rural households in each province. A total of 94 participants (i.e., seniors receiving home care services, their family/caregivers, and paid providers) were interviewed individually. In addition, 69 providers took part in focus groups. Analysis was iterative and concurrent with data collection in that each interview was compared with subsequent interviews for converging as well as diverging patterns. Results Six patterns were identified that provide a rich portrayal of the complexity of medication management safety in home care: vulnerabilities that impact the safe management and storage of medication, sustaining adequate supports, degrees of shared accountability for care, systems of variable effectiveness, poly-literacy required to navigate the system, and systemic challenges to maintaining medication safety in the home. Conclusions There is a need for policy makers, health system leaders, care providers, researchers, and educators to work with home care clients and caregivers on three key messages for improvement: adapt care delivery models to the home care landscape; develop a palette of user-centered tools to support medication safety in the home; and strengthen health systems integration.
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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 | 0.032 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".