Exploring Ethics in Practice: Creating Moral Community in Healthcare One Place at a Time
Bibliographic record
Abstract
Examining everyday ethical situations in clinical practice is a vital but often overlooked activity for nursing leaders and practitioners, as well as most other healthcare professionals. In this paper, we share how a series of practitioner-led Ethics in Practice sessions (EIPs), which originated within a busy urban teaching hospital, were adapted and translated, first into home care and more recently, into an EIP session for public health nurses. The success of EIP sessions rests with their focus on issues that are selected by practitioners. The aims of EIPs are to foster ethical leadership within communities of practice, create safe places to share concerns, use relevant research evidence and other literature to support informed discussion, and generate stories that deepen our understanding of the ethical situations we encounter in our work. We hope our experience inspires nursing leaders, nursing colleagues and fellow healthcare professionals to consider using the EIP approach to build moral community and the idea of moral imagination with their clinical colleagues, one place at a time.
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 | 0.059 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.026 | 0.070 |
| Scholarly communication | 0.023 | 0.023 |
| Open science | 0.004 | 0.035 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".