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Record W4412500295 · doi:10.1080/19411243.2025.2531328

Addressing the ethical issues of school-based occupational therapy practice: Perspectives from Quebec-Canada

2025· article· en· W4412500295 on OpenAlexafffundabout
Sandrine Renaud, Léa Bisaillon, Nancy Baril, Marie-Josée Drolet

Bibliographic record

VenueJournal of Occupational Therapy Schools & Early Intervention · 2025
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersUniversité du Québec à Trois-Rivières
KeywordsOccupational therapyOccupational scienceMedical educationPsychologyMedicinePedagogyNursingPhysical therapy

Abstract

fetched live from OpenAlex

School-based occupational therapists often encounter ethical issues that challenge core professional values. When unresolved, these ethical issues can have significant negative consequences. However, solutions to address them are not well-documented in existing literature. This study aimed to describe the solutions used or considered by school-based occupational therapists to address the ethical issues encountered in their practice. A qualitative inductive research design inspired by Husserl’s descriptive phenomenology was employed. Nineteen school-based occupational therapists practicing in Quebec, Canada were interviewed individually. Data analysis revealed 31 distinct themes. The results indicate that participants used 20 different solutions to address specific categories of ethical issues they face. Most of these solutions targeted ethical issues emerging from the micro-environment and are individual in nature. Additionally, the study identified 11 potential solutions applicable across all categories of ethical issues, the majority of which are structural in nature. By providing a comprehensive overview of these solutions, this study offers valuable insights and practical tools for occupational therapists to address the ethical issues of school-based practice.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.211
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.289
GPT teacher head0.567
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes3
Has abstractyes

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