The Use of Vignettes to Improve the Validity of Qualitative Interviews for Realist Evaluation
Bibliographic record
Abstract
Although realist evaluation (RE) requires multiple data collection methods, qualitative interviews are considered most valuable and are most frequently used. The guiding principles of RE may limit the emergence of new Context-Mechanism-Outcome (CMO) configurations by evoking particular underlying mechanisms. This paper proposes a new method for conducting semi-structured interviews in the RE context by drawing on the literature and examining the ability of vignettes to explore perceptions about specific situations. Vignettes are developed based on researchers' knowledge of the setting and program theory and are updated through an iterative process throughout data collection. Interviews focus on situations illustrated in the vignette to capture variations in interviewees' perceptions. This method constrains interviewees to using retroduction to identify the hidden underlying mechanisms that link contextual elements to outcomes based on their experiences. This method allows researchers to focus on CMO configurations without evoking mechanisms, which contributes to the rigor of the method.
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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 | 0.335 | 0.578 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".