Developing a New Clinical Ethics Framework for Rehab: A Pre-Implementation Evaluation from the Perspective of Future Users
Bibliographic record
Abstract
Clinical ethics is widely recognised as an essential contribution to the quality of health and psychosocial service delivery. However, the lack of a common understanding of ethics within teams and insufficient organisational support often limits its optimal integration into the workplace. To address this problem, the clinical ethics committee of a rehabilitation centre developed a new clinical ethics framework based on a theoretical model and conducted a pre-implementation evaluation by interviewing future users. The study estimated the acceptability and initial adoption of the new clinical ethics framework. The quantitative results of the study indicated a high level of acceptability for the definitions, tools and supporting strategies, with the exception of the definition of the concept of ethical issues. The qualitative results showed that the future users perceived positively the attributes of the new framework, such as its benefits and its compatibility with their professional concerns. In addition, they appreciated the fact that the framework was easy to understand and could potentially be applied in daily practice. The suggestions provided by future users also helped to improve the content of the clinical ethics framework. Finally, all the results will be useful for the planification of its eventual implementation.
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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 | 0.123 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".