A Patient-Centered Care Ethics Analysis Model for Rehabilitation
Bibliographic record
Abstract
There exists a paucity of ethics resources tailored to rehabilitation. To help fill this ethics resource gap, the authors developed an ethics analysis model specifically for use in rehabilitation care. The Patient-Centered Care Ethics Analysis Model for Rehabilitation is a process model to guide careful moral reasoning for particularly complex or challenging matters in rehabilitation. The Patient-Centered Care Ethics Analysis Model for Rehabilitation was developed over several iterations, with feedback at different stages from rehabilitation professionals and bioethics experts. Development of the model was explicitly informed by the theoretical grounding of patient-centered care and the context of rehabilitation, including the International Classification of Functioning, Disability and Health. Being patient centered, the model encourages (1) shared control of consultations, decisions about interventions, and management of the health problems with the patient and (2) understanding the patient as a whole person who has individual preferences situated within social contexts. Although the major process headings of the Patient-Centered Care Ethics Analysis Model for Rehabilitation resemble typical ethical decision-making and problem-solving models, the probes under those headings direct attention to considerations relevant to rehabilitation care. The Patient-Centered Care Ethics Analysis Model for Rehabilitation is a suitable tool for rehabilitation professionals to use (in real time, for retrospective review, and for training purposes) to help arrive at ethical outcomes.
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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 | 0.042 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".