Communicating Qualitative Research Study Designs to Research Ethics Review Boards
Bibliographic record
Abstract
Researchers using qualitative methodologies appear to be particularly prone to having their study designs called into question by research ethics or funding agency review committees. In this paper, the author considers the issue of communicating qualitative research study designs in the context of institutional research ethics review and offers suggestions for researchers to consider in their communication of study designs to research ethics review boards. General information about the mandate of research ethics review boards is provided. In light of wide international variability with respect to research ethics regulatory environments and review board processes, specific considerations and suggestions about communicating qualitative study designs effectively are presented within a Canadian case study example.
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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 | 0.739 | 0.825 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.017 | 0.024 |
| Scholarly communication | 0.022 | 0.024 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.012 | 0.020 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".