“Thought provoking”, “interactive”, and “more like a peer talk”: Testing the deliberative interview style in Germany
Bibliographic record
Abstract
Proponents of the newly-developed "deliberative interview" argue that examining complex issues requires more dynamic and engaging interview exchanges. Unlike traditional qualitative interviews, deliberative interviewing champions opinion sharing, active debates and similar speaking times by both sides throughout the interview. Drawing on 20 interviews with health experts in Germany, we examined the process and outcome of deliberative versus conventional interviews on the topic of informed consent. The deliberative interview expedited clarity on the issue, led to more nuanced discussion and generated more knowledge overall, but was challenging because it broke the mold for traditional interviewing. Alignment in terms of gender, age, personality and professional background facilitated rapport, regardless of interview style. To manage expectations, we recommend a thorough, perhaps video-based explanation of the deliberative style prior to the interview. Deliberative interviews can bolster knowledge generation for complex issues and can be applied in public health and beyond.
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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 | 0.166 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".