Interprofessional collaboration-in-practice
Bibliographic record
Abstract
The main question examined is: How do nurses and other healthcare professionals ensure ethical interprofessional collaboration-in-practice as an everyday practice actuality? Ethical interprofessional collaboration becomes especially relevant and necessary when interprofessional practice decisions are contested. To illustrate, two healthcare scenarios are analyzed through three ethics lenses. Biomedical ethics, relational ethics, and virtue ethics provide different ways of knowing how to be ethical and to act ethically as healthcare professionals. Biomedical ethics focuses on situated, reflective, and nonabsolute principled justification, all things considered; relational ethics on intersubjective, professional, and institutional relations; and virtue ethics on prephilosophical tradition and what it means to be good and to be human embedded in social and political community. Analysis suggests that interprofessional collaboration-in-practice may be more rhetoric than actuality. Key challenges of interprofessional collaboration-in-practice and specific conditions perpetuating dissension and conflict are outlined with specific education and policy recommendations included.
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 | 0.053 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".