Do Residents Need All Their Medications? A Cross-Sectional Survey of RNs' Views on Deprescribing and the Role of Clinical Pharmacists
Bibliographic record
Abstract
A cross-sectional survey was mailed to 307 RNs of a nationally representative sample of residential aged care facilities to investigate their views and perceptions on medication use and deprescribing in older adults. Questions were grouped according to each stage of the medication use process, and a dedicated section to explore nurses' views on deprescribing was included. Ninety-one questionnaires were received, yielding a 29.6% response rate. Respondents highlighted several challenges including achieving medication reconciliation for new residents, access to physicians to admit patients in a timely fashion, and issues pertaining to lack of clear medical information transcribing when transferring patients between health care settings. More than one half (67.4%) of nurses agreed or strongly agreed that deprescribing implemented with the help of a clinical pharmacist would be beneficial to residents and could improve medication adherence (44%), benefit residents' quality of life (50.5%), and reduce the length of time spent by nurses on medication administration (35.2%). Increased awareness regarding polypharmacy and potential deprescribing benefits is necessary to improve appropriate prescribing and medication use. [Journal of Gerontological Nursing, 43(10), 13-20.].
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 | 0.003 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".