Neuro-Advancements and the Role of Nurses as Stated in Academic Literature and Canadian Newspapers
Bibliographic record
Abstract
Neurosciences and neurotechnologies (from now on called neuro-advancements) constantly evolve and influence all facets of society. Neuroethics and neuro-governance discourses focus on the impact of neuro-advancements on individuals and society, and stakeholder involvement is identified as an important aspect of being able to deal with such an impact. Nurses engage with neuro-advancements within their occupation, including neuro-linked assistive technologies, such as brain-computer interfaces, cochlear implants, and virtual reality. The role of nurses is multifaceted and includes being providers of clinical and other health services, educators, advocates for their field and their clients, including disabled people, researchers, and influencers of policy discourses. Nurses have a stake in how neuro-advancements are governed, therefore, being influencers of neuroethics and neuro-governance discourses should be one of these roles. Lifelong learning and professional development could be one mechanism to increase the knowledge of nurses about ethical, social, and legal issues linked to neuro-advancements, which in turn, would allow nurses to provide meaningful input towards neuro-advancement discussions. Disabled people are often the recipients of neuro-advancements and are clients of nurses, therefore, they have a stake in the way nurses interact with neuro-advancements and influence the sociotechnical context of neuro-advancements, which include neuro-linked assistive devices. We performed a scoping review to investigate the role of narrative around nurses in relation to neuro-advancements within academic literature and newspapers. We found minimal engagement with the role of nurses outside of clinical services. No article raised the issue of nurses having to be involved in neuro-ethics and neuro-governance discussions or how lifelong learning could be used to gain that competency. Few articles used the term assistive technology or assistive device and no article covered the engagement of nurses with disabled people within a socio-technical context. We submit that the role narrative falls short of what is expected from nurses and shows shortcomings at the intersection of nurses, socio-technical approaches to neuro-assistive technologies and other neuro-advancements and people with disabilities. Neuro-governance and neuroethic discourses could be a useful way for nurses and disabled people to co-shape the socio-technical context of neuro-advancements, including neuro-assistive technologies. Lifelong learning initiatives should be put in place to provide the knowledge necessary for nurses to take part in the neuroethics and neuro-governance discussion.
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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 | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.013 | 0.024 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".