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Record W2034313405 · doi:10.1177/0308022614564770

Ethical tensions in occupational therapy practice: A scoping review

2015· review· en· W2034313405 on OpenAlexaff
Kathryn Bushby, Jody Chan, Shawna Druif, Kim Jae Ho, Elizabeth Anne Kinsella

Bibliographic record

VenueBritish Journal of Occupational Therapy · 2015
Typereview
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsWestern University
Fundersnot available
KeywordsOccupational therapyEngineering ethicsFoundation (evidence)Interpersonal communicationEthical issuesPsychologySystematic reviewMedicineMedical educationMEDLINEPolitical scienceSocial psychologyPsychiatryEngineering

Abstract

fetched live from OpenAlex

Introduction Ethical tensions are an unavoidable part of occupational therapy practice. Given the significance of this, and the divergent nature of the literature, a need was identified for a systematic examination of the published literature on this topic. The purpose of this scoping review therefore was to identify, summarize, and describe existing literature on ethical tensions in occupational therapy practice. Method A comprehensive scoping review was conducted. In the initial search, 459 articles, spanning a 13-year time frame, were retrieved from six databases; 32 articles met the criteria for full review. Results Seven themes were identified that highlighted ethical tensions related to: (a) resource and systemic issues; (b) upholding ethical principles and values; (c) client safety; (d) working with vulnerable clients; (e) interpersonal conflicts; (f) upholding professional standards and (g) practice management. Conclusion This scoping review highlights a breadth of ethical tensions that have implications for practice, education, policy and research. It represents an important first step in mapping knowledge about ethical tensions in occupational therapy practice, and lays a foundation for future research directions.

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)0.048
metaresearch head score (Gemma)0.081
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.906
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0480.081
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0020.026
Insufficient payload (model declined to judge)0.0020.000

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 head0.588
GPT teacher head0.668
Teacher spread0.080 · 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; both teacher heads agree on what is shown here.

Study designOther design
Domainnot available
GenreReview

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

Citations70
Published2015
Admission routes1
Has abstractyes

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