Perspectives of Canadian Researchers on Ethics Review of Neuroimaging Research
Bibliographic record
Abstract
The current and potential uses of neuroimaging in healthcare and beyond have spurred discussion about the ethical issues related to neuroimaging and neuroimaging research. This study examined the perspectives of neuroimagers on ethical issues in their research and on the ethics review process. One hundred neuroimagers from 13 Canadian neuroscience centers completed an online survey and 35 semi-structured interviews were conducted. Neuroimagers felt that most ethical and social issues identified in the literature were dealt with adequately, well, and even very well by research ethics boards (REBs), but some issues such as incidental findings and transfer of knowledge were problematic. Neuroimagers reported a range of practical problems in the ethics review process. We aimed to gather perspectives from REB on the ethics review process, but insufficient participation by REBs prevented us from reporting their perspectives. Given shortcomings identified by neuroimagers as well as longstanding issues in Canadian ethics governance, we believe that substantial challenges exist in Canadian research ethics governance that jeopardize trust, communication, and the overall soundness of research ethics governance. Neuroimagers and REBs should consider their shared responsibilities in developing guidance to handle issues such as incidental findings, risk assessment, and knowledge transfer.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: no | Qualitative | medium |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Qualitative | medium |
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.527 | 0.839 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.020 | 0.015 |
| Science and technology studies | 0.002 | 0.021 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.004 | 0.241 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".