Nurses' attitudes and knowledge regarding patient rights: a systematic review
Bibliographic record
Abstract
OBJECTIVE: To synthesize current evidence on nurses' attitudes and/or knowledge on the entire spectrum of patient rights. METHOD: A systematic search of the literature was performed in Web of Science, PubMed, Scopus and CINAHL. Studies were selected according to pre-defined inclusion/exclusion criteria. The Cochrane and PRISMA guidelines, including templates for systematic reviews, were applied. For rigor assessment, the Critical Appraisal Skills Program Qualitative Research Checklist, and the Center for Evidence-Based Management tool were employed. RESULTS: Thirteen studies were included, that exhibited important methodological limitations, such as convenience sampling, mediocre response rates and inadequate instrument validity. Findings indicated: a) low level of awareness regarding patient rights among nurses, b) knowledge discrepancies on specific aspects of patient rights, c) low priority ascribed to a patient's right to access information, and d) insufficient evidence on formal educational sources of knowledge on the topic of patient rights. CONCLUSION: Narrow geographical localization, heterogeneity and methodological limitations render generalizability of the conclusions difficult. Further research based on robust methodology is proposed.
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 | 0.015 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".