Extending the Olive Branch: Enhancing Communication and Trust Between Research Ethics Committees and Qualitative Researchers
Bibliographic record
Abstract
Using data from a study of qualitative researchers' experiences with ethics review and our own lens as both researchers and REB/IRB members, we explore the ethics review process and provide recommendations for improvements. Our findings suggest that the review process would benefit from a strengthened trust relationship between REB/IRBs and qualitative researchers that would require a commitment from both sides. Regarding REBs/IRBs, increased transparency of the review process, consistent application of federal guidelines, and a more collaborative review approach may improve the trust of qualitative researchers. Regarding researchers, approaching ethics review as a form of academic peer review, similar to other types of assessments of scholarly products such as grants and publications, may promote the integration of ethics review as an intrinsic part of the research process. Recognizing that responsibility for ethical research is a shared goal of both researchers and REB/IRBs, improved collaboration and constructive interaction can assist in understanding each other's perspective and work toward the development of mutual trust and respect.
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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: no · About a Canadian topic: no | Qualitative | low |
| gpt | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | medium |
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 | 0.392 | 0.472 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.027 | 0.029 |
| Scholarly communication | 0.020 | 0.039 |
| Open science | 0.004 | 0.056 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".