Occupationaljustice and Client-Centred Practice: A Dialogue in Progress
Bibliographic record
Abstract
BACKGROUND: This paper describes an ongoing, international dialogue about the relationship between occupation, justice, and client-centred practice, prompted by the question: How do occupational therapists work for justice? METHODS: The authors critically reflect on their own dialogue and culturally situated interests, dialogues with workshop participants, and a literature review. RESULTS: Four cases of occupational injustice are proposed: occupational alienation, occupational deprivation, occupational marginalization, and occupational imbalance. The naming of these occupational injustices suggests four occupational rights: to experience meaning and enrichment in one's occupations; to participate in a range of occupations for health and social inclusion; to make choices and share decision-making power in daily life; and to receive equal privileges for diverse participation in occupations. PRACTICE IMPLICATIONS: Since silence implies compliance with the status quo, the authors encourage occupational therapists to develop their own dialogue about occupational injustices in order to address them openly with others. Dialogue about occupational justice is timely as occupational therapists around the world articulate what distinguishes this numerically small, rather invisible profession and its contributions to individuals, populations, and societies.
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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 | 0.032 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".