Safety checks, monitoring and documentation in medication process in long-term elderly care–Nurses' subjective perceptions
Bibliographic record
Abstract
Objective: Elderly people often use several medicines, which increases risks for side effects and adverse effects. Moreover, most reported adverse events in healthcare are associated with medication. The aim was to describe nursing staffs’ perceptions about and the factors related to the actualization of safety checks, monitoring and documentation in the medication process in long-term elderly care.Methods: This was a cross-sectional quantitative, questionnaire-based study. The response rate, among all nurses working in long-term elderly care wards in a Finnish healthcare district, was 39.4% (n = 492).Results: The results indicate that some safety checks and monitoring guidelines are often violated during the medication administration process, but most nurses self-reportedly maintained good practice in medication documentation.Conclusions: The results suggest needs to review training in pharmacology, infection control, and medication calculations during pre-qualification and continuing education, and to ensure nurses’ awareness of attitudes and ethical considerations for medication safety.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".