Influencing discussions and use of neuroadvancements: Perspectives of Canadian occupational therapists
Bibliographic record
Abstract
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.
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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 | 0.019 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.061 | 0.025 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".