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Record W4289443845 · doi:10.3233/wor-205108

Influencing discussions and use of neuroadvancements: Perspectives of Canadian occupational therapists

2022· article· en· W4289443845 on OpenAlexaffabout
Valentina Villamil, Manel Djebrouni, Gregor Wolbring

Bibliographic record

VenueWork · 2022
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsOccupational therapyPsychologyMedical educationEngineering ethicsMedicineEngineeringPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: The early involvement of many actors including health professionals is identified in neuroethics and neurogovernance discussions as crucial in constructing conversations around awareness, reaction, and knowledge development pertaining to the ethical, legal, and societal consequences of neuroscientific or neurotechnological advancements (NA). Occupational Therapists (OTs) have a stake in NA; however, OTs are rarely mentioned within this context. Lifelong learning (LL) could be used to increase OTs knowledge on NA and its consequences. However, LL is rarely mentioned within neuroethics and neurogovernance discussions. OBJECTIVE: The study's purpose is to understand the role of OTs as professionals and citizens in neuroethics and neurogovernance discussions and to examine the utility of LL processes put in place for OTs to empower OTs to contribute in a meaningful way to NA discussions. METHODS: 8 semi-structured interviews with OTs were conducted and analyzed using a directed content analysis. RESULTS: Although participants believed OTs can provide a holistic perspective to neurogovernance discussions, their knowledge on NA and its consequences is limited, and LL is not used as a tool to remain informed about such consequences. CONCLUSION: More education on NA and its consequences throughout their OT degree and through LL opportunities is warranted to facilitate their involvement.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.060
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.120
GPT teacher head0.444
Teacher spread0.324 · 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
Published2022
Admission routes2
Has abstractyes

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