Navigating Ethical Tensions When Working to Address Social Inequities
Bibliographic record
Abstract
IMPORTANCE: When providing services, occupational therapists encounter social inequities that affect the health and well-being of their clients and create ethical tensions. OBJECTIVE: To develop an understanding of the ethical tensions encountered by occupational therapists working with clients experiencing social inequity and how such tensions are navigated. DESIGN: This qualitative study used an interpretive description methodology. SETTING: Community and tertiary health settings. PARTICIPANTS: Fifteen occupational therapists who identified as working with clients experiencing social inequity. OUTCOMES AND MEASURES: Semistructured interviews were used to explore participants' practice experiences. Data were analyzed using thematic analysis. RESULTS: Two themes were identified in relation to participants' experiences of ethical tensions: (a) perpetuating inequities and (b) experiencing conflicting values. A further three themes were identified in relation to how participants identified and navigated these tensions: (a) taking action, (b) seeking support, and (c) ensuring integrity and accountability. CONCLUSIONS AND RELEVANCE: Ethical tensions frequently emerged when systemic health contexts were not responsive to social inequities or created barriers to health care access. Occupational therapists felt a sense of responsibility to take action to address inequity, which led them to stretch boundaries and roles. Having informal and formal supports, and confidence in the scope of their practice, helped the occupational therapists to navigate ethical tensions with integrity and accountability. Increased avenues for support that incorporate reflexivity offer an opportunity for occupational therapists to engage in dialogue about social inequities and ethical practice. What This Article Adds: This article explores the types of ethical tensions occupational therapists experience when addressing social inequities and provides insights into how such tensions are managed.
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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 | 0.058 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.055 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".