Registered Nurses’ Experiences of Medication Errors—An Original Research Protocol: Methodology, Methods, and Ethics
Bibliographic record
Abstract
BACKGROUND: The investigation of medication errors in nursing includes both methodological and ethical considerations because it is a sensitive field of research. PURPOSE: To present an original research protocol for the investigation of nurses' experiences of medication errors with interpretative phenomenological analysis and the relevant methodological and ethical considerations. METHODS: A discursive paper which presents an original research protocol about nurses' experiences of medication errors with interpretative phenomenological analysis followed by a literature review and personal reflections about the relevant methodological and ethical considerations. The review included papers published in English from 1990 to February 2019 on PubMed, BNI (British Nursing Index), CINAHL (Cumulative Index to Allied Health Literature), ScienceDirect, and Wiley Online Library. RESULTS: The following methodological considerations were identified: recruitment of participants, data collection, and data analysis, and the ethical considerations included researcher's morality, ethics committees, sensitivity, phrasing of sentences and words, recruitment of participants, location of interviews, type of interviews, emotionality management, medication error incidents' management, researcher, or nurse? CONCLUSION: By facing as many as possible methodological and ethical considerations and establishing solutions for them, the study's validity, reliability, and rigor are enhanced, and the study is ethically robust. Finally, their understanding enables researchers to uncover nurses' experiences and interpret the meanings they generate in depth.
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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 | 0.150 | 0.167 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".