The Unstructured Interactive Interview: Issues of Reciprocity and Risks when Dealing with Sensitive Topics
Bibliographic record
Abstract
Qualitative research using unstructured interviews is frequently reviewed by institutional review boards using criteria developed for biomedical research. Unlike biomedical studies, unstructured interactive interviews provide participants considerable control over the interview process, thereby creating a different risk profile. This article examines the interview process and literature for evidence of benefit and harm. Although there is evidence that qualitative interviews may cause some emotional distress, there is no indication that this distress is any greater than in everyday life or that it requires follow-up counseling, although the authors acknowledge distress is always a possibility. Essential to preventing participant distress is the researcher's interviewing skills and a code of ethics. When research is conducted with sensitivity and guided by ethics, it becomes a process with benefits to both participants and researchers. The authors conclude that qualitative research using unstructured interviews poses no greater risk than everyday life and expedited reviews are sufficient.
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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 | 0.424 | 0.499 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.015 | 0.042 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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, 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".