Developing a Collaborative Understanding of Health Justice in Physiotherapy: Findings from a National Consensus Development Conference
Bibliographic record
Abstract
Purpose: Justice and health equity are necessary to ensure the health and wellness of an optimally operating society. Health care leaders, educators, students, and clinicians should endeavour to achieve health justice; however, there is a paucity of literature exploring health justice and similarly, a lack of accepted models or frameworks to actualize this state. There is a need to understand the tenets of health justice that can be integrated across and within the physiotherapy profession. The aims of this project were to build upon a proposed operational definition of health justice through a national consensus exercise and identify concepts related to health justice that could inform physiotherapy education and practice. Method: A facilitated 3-hour virtual consensus development conference was held on November 25, 2022, and included three rounds of discussion and voting. A total of 34 delegates across targeted organizations consented to participate in this study. Participants represented delegates across key Canadian physiotherapy organizations, students, educational and health service administrators, and clinicians across various health care disciplines. Results: Facilitated discussion within conference rounds informed revisions to the originally proposed definition of health justice. Seventeen concepts met consensus to be included in a collaborative understanding of health justice. These concepts listed in alphabetical order were accessibility, affordability, availability, determinants of health, diversity, equity, inclusion, intersectionality, health, health equity, oppression, power, privilege, quality, racism, social equity, and sustainable health. A post-conference survey resulted in the inclusion of two additional concepts, bias and voice, for a total of 19 included concepts. Ten concepts requiring further exploration were identified. Conclusions: This national consensus exercise included interprofessional delegates from physiotherapy organizations, academia, and clinical practice. Conference results can inform the development of curricular content, policies, procedures, and practices by stakeholders in physiotherapy and other health care disciplines.
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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 | 0.236 | 0.329 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".