Role Expectations for Nurses and Neuroscientific and Neurotechnological Advancements: A Qualitative Study on the Perceptions of Nurses on Their Roles and Lifelong Learning
Bibliographic record
Abstract
Nurses can understand and evaluate the impact of neuroscientific and neurotechnological advancements (NNA) from multiple standpoints given their roles as patient liaisons, advocates for their profession and their clients, leaders, and educators as well as their interactions with NNA such including deep brain stimulation and neuroimaging. We conducted semi-structured interviews with Canadian nurses to understand their perspectives on their roles in and outside the workplace, their familiarity with NNA and their ethical, legal, and social implications, their participation in NNA governance discussions and how lifelong learning can be applied to empower their participations in NNA governance discussions. Participants felt that nurses had the potential to meaningfully involve themselves in NNA governance discussions if given a greater opportunity. Participants were not offered lifelong learning surrounding the ethical, legal, and social implications of NNA. It would be fruitful to facilitate and empower nurses as contributors to NNA governance and ethics discussions.
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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 | 0.026 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".