Nurses’ perceptions of verification of medication competence
Bibliographic record
Abstract
Objective : Medication administration is a common clinical procedure of nurses. However, medication errors are a significant cause of morbidity and mortality in hospitalized patients. Previous studies have shown that nurses lack theoretical knowledge and drug calculation skills. This challenges nurses to update their skills regularly and hospitals to organise a systematic verification process of medication competence. The Finnish Ministry of Social Affairs and Health defined in 2006 how nurses’ medication competence should be verified. Hence, Finnish nurses’ perceptions of the verification process of medication competence was considered a significant topic to be studied. Methods : The study has a qualitative descriptive design and the data were analysed using inductive content analysis. Results : Two main categories and nine generic categories were generated from collected data. Five of the generic categories contain nurses’ perceptions of how they accept the verification process as part of their work. Four of the generic categories contain nurses’ perceptions of barriers to successful implementation of the verification process. Conclusions : Nurses considered the verification process of medication competence important to developing medication safety and practices. Nurses considered that the verification process maintains and improves their medication competence. E-learning is a sound method of implementing the process but nurses suggest additional lectures and workshops, e.g. on drug calculations. Nurses appreciate the mandatory nature of the verification process as long as they perceive the verified competence meaningful to their professional role as nurses.
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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 | 0.014 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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".