A review of factors that contribute to sub‐optimal medication management for older adult with dementia in residential care settings: Nurses’ perspectives
Bibliographic record
Abstract
Abstract Background Medication use can play an important role in the health and well‐being of people with dementia in the long‐term care facilities. However, nurses often find the process of managing medicines for this population to be complex and challenging. As a result, various strategies are being employed to facilitate the medication management process including altering or modifying medications, hiding medication in food or drink of residents, and deviation from the ‘rights’ of medication administration (Barnes et al., 2006; Mc Gillicuddy et al., 2017; Mercovich et al., 2014; Qian et al. 2018; Serrano Santos et al., 2016; Thomson et al., 2009; Verrue et al., 2011; Wright, 2002). These practices are associated with increased risk of medication errors (Kartunen et al., 2019). Despite this, factors contributing to these practices are not well understood. Method A comprehensive search of CINAHL, Medline, SOCIndex, PubMed, and PsychINFO databases was conducted between November and December 2019. ProQuest Dissertations and Theses and Google Scholar were used to search for gray literature. No date limitation was applied. A modified form of standardized critical appraisal tool known as mixed method appraisal tool (MMAT) was used to assess the quality of the papers (Hong, Gonzalez‐Reyes, & Pluye, 2018). Nvivo was used to extract findings. Data synthesis was done using convergent synthesis approach, more specifically, thematic analysis (Hong et al. 2017). Result The search resulted in 806 unique titles for screening. A total of 96 abstracts were screened, after which a full‐text review conducted for 59 articles. After the full‐text review, 12 articles were eligible to be included in the final report. The findings highlighted 2 major factors: individual and system factors Conclusion There is need for both individual and system interventions to facilitate safe medication management for residents with dementia.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".