Moral Agency as Enacted Justice: A Clinical and Ethical Decision-Making Framework for Responding to Health Inequities and Social Injustice
Bibliographic record
Abstract
This is the second of 2 companion articles in this issue. The first article explored the clinical and ethical implications of new emphases in physical therapy codes of conduct reflecting the growing evidence regarding the importance of social determinants of health, epidemiological trends for health service delivery, and the enhanced participation of physical therapists in shaping health care reform in a number of international contexts. The first article was theoretically oriented and proposed that a re-thinking of ethical frameworks expressed in codes of ethics could both inform and underpin practical strategies for working in primary health care. A review of the ethical principle of "justice," which, arguably, remains the least consensually understood and developed principle in the ethics literature of physical therapy, was provided, and a more recent perspective-the capability approach to justice-was discussed. The current article proposes a clinical and ethical decision-making framework, the ethical reasoning bridge (ER bridge), which can be used to assist physical therapy practitioners to: (1) understand and implement the capability approach to justice at a clinical level; (2) reflect on and evaluate both the fairness and influence of beliefs, perspectives, and context affecting health and disability through a process of "wide reflective equilibrium" and assist patients to do this as well; and (3) nurture the development of moral agency, in partnership with patients, through a transformative learning process manifest in a mutual "crossing" and "re-crossing" of the ER bridge. It is proposed that the development and exercise of moral agency represent an enacted justice that is the result of a shared reasoning and learning experience on the part of both therapists and patients.
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 | 0.063 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.130 |
| Scholarly communication | 0.030 | 0.024 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.024 | 0.023 |
| Insufficient payload (model declined to judge) | 0.005 | 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".