When words fail us: An integrative review of innovative elicitation techniques for qualitative interviews
Bibliographic record
Abstract
INTRODUCTION: Interviews are central to many qualitative studies in health professions education (HPE). However, researchers often struggle to elicit rich data and engage diverse participants who may find this strategy exclusionary. Elicitation techniques are strategies tailored to address these challenges, enhancing oral conversations through other forms of interaction-for example, participant photography and neighbourhood walks. These strategies are tailored to elicit the rich data needed to address complex problems and meaningfully engage participants. Unfortunately, guidance on these techniques is scattered across literatures from diverse fields. In this synthesis, we offer an overview of the elicitation techniques available and advice about how to choose between them. METHODS: We conducted an integrative review, drawing on methodological literature from across the health and social sciences. Our interdisciplinary searches yielded 3056 citations. We included 293 citations that were methodologically focused and discussed elicitation techniques used in interviews with adults. We then extracted specific elicitation techniques, summarising each technique to capture key features, as well as strengths and weaknesses. From this, we developed a framework to help researchers identify challenges in their interview-based research and to select elicitation techniques that address their challenges. RESULTS: To enrich data, researchers might seek to shift conversations away from participants' entrenched narratives, to externalise conversations on sensitive topics, or to elicit affect, tacit knowledge or contextual details. When empowering participants, researchers might seek to increase equity between the researcher and participant or foster interview accessibility across diverse participant populations. DISCUSSION: When chosen with study goals in mind, elicitation techniques can enrich interview data. To harness this potential, we need to re-conceptualise interviews as co-production of knowledge by researcher(s) and participant(s). To make interviews more equitable and accessible, we need to consider flexibility so that each participant can engage in ways that best suit their needs and preferences.
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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 | 0.192 | 0.294 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.037 | 0.037 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| 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; 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".