Ethical Tensions in Transitioning to Tiered Models of School-Based Occupational Therapy
Bibliographic record
Abstract
Background. School-based occupational therapy (SBOT) is shifting from pull-out interventions for students with special needs to tiered models focused on inclusion and participation. There are several noted benefits of tiered models. However, research has suggested that challenges exist in the transition to tiered models that are consistent with ethical tensions. It is unclear how occupational therapists are navigating the transition to tiered models, including addressing ethical tensions. Purpose. The purpose was to advance knowledge and understanding regarding ethical tensions experienced by school-based occupational therapists in transitioning to tiered models of SBOT. The research question was: What are the perceived ethical tensions experienced by occupational therapists in transitioning to tiered models of service delivery in SBOT? Method. Interpretive description was employed. Interviews were conducted with 11 self-nominated occupational therapists. Data analysis consisted of preparation, organization, and interpretation followed by a member checking focus group. Findings. Occupational therapists experienced ethical tensions around five inter-related ethical principles—fidelity, veracity, autonomy, confidentiality, and distributive justice. Conclusion. The transition to tiered SBOT exacerbated or created ethical tensions. Engaging established implementation guidelines can provide a structured framework to inform large-scale service delivery changes, lessening ethical tensions while eliciting desired outcomes.
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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 | 0.054 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.022 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.007 |
| 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".