Bibliographic record
Abstract
Purpose The South West Health Ethics Network (SWHEN) was created to bring together health care providers from a variety of health care settings across a geographical region. SWHEN’s mission was to connect health professionals who have an interest in ethical issues. SWHEN’s target participants are people with an interest in this field regardless of the individual’s capacity within an ethics profession. While other ethics networks exist, few of these expand beyond a narrow scope of ethics professionals (clinical ethicists). The preliminary vision in bringing together this group was to create a regional collaborative to educate, share lessons and begin to create a common approach to ethics issues in our region. Ethics networks increase collaboration and the exchange of resources, information and ideas among clinical ethicists. As a result, they address many of the ethical dilemmas faced in integrated care and facilitate the success of these systems in providing coordinated patient care. The paper aims to discuss these issues. Design/methodology/approach A Delphi consensus building approach was conducted to determine goals and priorities of the network. Findings Several priorities and counter priorities were discussed. In the end, the network was stifled by three major challenges: resource sharing, balance of network priorities and individual needs, and leadership. Originality/value While the journey to creating a sustainable network is long and complex, it is still worth the struggles. Network members remained connected through e-platforms, and the meetings have increased our region’s cohesiveness around ethics. We remain cautiously optimistic of SWHENs future and acknowledge that our initial plan may have shifted but our achievements are still meaningful and worthwhile.
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 | 0.048 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.003 | 0.034 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".