Colliding Identities and the Act of Creating Spaces of Belonging in the Occupational Therapy Profession
Bibliographic record
Abstract
Introduction. Despite numerous initiatives to recruit a more diverse health professional workforce, those entering the health professions from marginalized groups experience significant barriers to inclusion. The occupational therapy (OT) profession is no exception. The profession, despite language of inclusion, is heavily influenced by colonialism and ableism, and positions itself largely under a Western world view. Literature points to OT students and clinicians from marginalized groups experiencing discrimination and racism, alienation, and internal conflicts between their own sense of identity and that which is expected in the OT profession. Lack of belonging can be a major barrier to success and fulfillment for those wishing to enter the profession. Objective. To highlight the invisible work done by those from marginalized groups to create spaces of belonging in the OT profession, through telling personal stories. Key Issues. Feelings of personal and professional belonging deeply impact the ways diverse OT students and clinicians engage meaningfully with themselves and their communities. Given the profession is currently aiming to identify its largely uninterrogated Western underpinnings, we must listen and learn from and with those from marginalized groups to create systemic, meaningful change. Implications. Creating community and supports within the profession in the context of a marginalized identity takes a significant amount of time and robust mentorship. We must begin to highlight this additional “invisible” work to create systemic changes and solutions and ease the burden for diverse peoples entering the profession.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.036 | 0.077 |
| Scholarly communication | 0.020 | 0.015 |
| Open science | 0.002 | 0.031 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".