Influencing discussions and use of neuroadvancements as professionals and citizens: Perspectives of Canadian speech-language pathologists and audiologists
Bibliographic record
Abstract
BACKGROUND: Early involvement of stakeholders in neuroethics and neurogovernance discourses of neuroscientific and neurotechnological advancements is seen as essential to curtail negative consequences. Speech-language pathologists (SLPs) and audiologists (AUs) make use of neuroadvancements including cochlear implants, brain-computer interfaces, and deep-brain stimulation. Although they have a stake in neuroethics and neurogovernance discussions, they are rarely mentioned in having a role, whether as professionals or as citizens. OBJECTIVE: The objective of the study was to explore the role of SLPs and AUs as professionals and citizens in neuroethics and neurogovernance discussions and examine the utility of lifelong learning mechanisms to learn about the implications of neuroadvancements to contribute in a meaningful way to these discussions. METHODS: Semi-structured interviews conducted with 7 SLPs and 3 AUs were analyzed using thematic analysis. RESULTS: Participants stated that their roles expected from them as professionals and as citizens indicate the importance to be knowledgeable on ethical, legal, and social implications of neuroadvancements and that lifelong learning is not used to learn about these implications. CONCLUSION: More must be done to facilitate the participation of SLPs and AUs in neuroethics and neurogovernance discussions, which would enrich the neuroethics and neurogovernance discourses benefitting patients, professionals, and the public.
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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 | 0.037 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.063 | 0.035 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".