Occupational therapists speaking for justice and human rights: From complicit silencing to collective resistance
Bibliographic record
Abstract
We are a Collective of occupational therapists from all areas of practice and roles with longstanding commitments to working for human rights in the context of colonial oppressions, including genocide. We aim to affirm our deep commitment to occupational therapy’s values and principles by naming and acting against the ongoing genocide of Palestinian people. This paper is an act of collective resistance, self-assertion and solidarity for all those in the profession who have been erased, denied, and harmed by the silence of occupational therapy’s national and international governing bodies, as well as the silencing. We write this piece anonymously to add our voices to those healthcare professionals who are speaking out – including those working in healthcare in Gaza. We begin by naming the injustice, citing recent evidence and the rulings of various international bodies considered authoritative and legitimate. Then we address the injustice of silence, as experienced in the occupational therapy profession through the silencing of its members. We continue by reviewing relevant professional obligations and ethical responsibilities of occupational therapists, including the requirement to speak out against occupational injustices. This is followed by an outline and critique of our profession’s governing bodies’ problematic responses and complicity, naming the harms caused from within. We conclude this paper by indicating the only ethical option for action; one that also applies to other healthcare professions whose institutions have been silent, hoping this will incite them to break their silence.
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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 | 0.023 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.085 |
| Scholarly communication | 0.024 | 0.015 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.010 | 0.013 |
| 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".