Ethical issues identified in nurses´ interprofessional collaboration in clinical practice: a meta-synthesis
Bibliographic record
Abstract
The aim of this study was to synthesize previous knowledge about ethics in nurses' interprofessional collaboration in clinical practice. Although healthcare professionals have common goals and shared values, ethical conflicts still arise during patient care. We carried out a meta-synthesis of peer-reviewed papers published in any language from 2013-2019, using both electronic searches, with the CINAHL, PubMed, Scopus, and SocINDEX databases, and manual searches. We identified 4,763 papers and selected six qualitative papers, and three theoretical papers, based on predetermined inclusion and exclusion criteria and quality appraisal. The studies came from the USA, Canada, Sweden, Australia, Botswana, and the Netherlands. We found that in ethics studies on nurses' interprofessional collaboration in clinical practice the focus has been on factors that affect how patients receive care. These factors were patients' wishes, whether they were told the truth about their condition, and how different professionals recognized and treated their pain. The focus in the papers we reviewed was on the roles of different professionals during the care process, including ethical conflicts with regard to their aims, commitment, and the balance of power among them and other professions. More research is needed to raise the visibility of how nurses and other professionals recognize, and evaluate, their professional and interprofessional ethics.
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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 | 0.107 | 0.283 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.026 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".