Discourse / Discours - Medication Management for Nurses Working in Long-Term Care
Bibliographic record
Abstract
In long-term care (LTC), the complexity of residents' conditions and their treatment requirements present challenges for nurses managing medications. The purpose of this qualitative descriptive study was to explore medication management as described by licensed nurses working in LTC. A total of 22 licensed nurses from 2 LTC facilities located in the Canadian province of Ontario participated in 4 focus groups. Thematic content analysis was used to organize data into themes and a conceptual model was developed. The overarching theme was that nurses are racing against to manage medications and 3 subthemes described how they coped with this important care process: preparing to race, running the race, and finishing the race. Barriers to safe medication management included time restraints, knowledge limitations, interruptions and distractions, and poor communication. The findings can be used to better inform health-care providers and to guide future research. They also have the potential to directly impact outcomes related to safe medication management in LTC.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".