How Not to Let Secrets Out When Conducting Qualitative Research With Dyads
Bibliographic record
Abstract
Confidentiality is one of the cornerstones of research involving human participants. Researchers are the frontline gatekeepers of their participants' right to confidentiality, and situations can arise that challenge this responsibility. This is the case when individuals who have shared a common experience (i.e., dyads) are interviewed separately, but interview results are disseminated within the context of dyads. Based on our experience of conducting research with dyads and given how little literature is available to serve as guide, we set out to write this article to share the knowledge we acquired and the solutions we found. We will describe both the ethical challenges and the methodological decisions involved in conducting qualitative research with dyads. The article also describes different modalities of dyadic analysis, their benefits and drawbacks. This endeavor seems especially relevant as research with dyads is emerging in several domains involving couples, families, caregivers and health.
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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: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.583 | 0.629 |
| Meta-epidemiology (narrow) | 0.003 | 0.006 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.031 | 0.073 |
| Scholarly communication | 0.032 | 0.050 |
| Open science | 0.011 | 0.027 |
| Research integrity | 0.016 | 0.028 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".