Development and use of research vignettes to collect qualitative data from healthcare professionals: a scoping review
Bibliographic record
Abstract
OBJECTIVES: To clarify the definition of vignette-based methodology in qualitative research and to identify key elements underpinning its development and utilisation in qualitative empirical studies involving healthcare professionals. DESIGN: Scoping review according to the Joanna Briggs Institute framework and Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines. DATA SOURCES: Electronic databases: Academic Search Complete, CINAHL Plus, MEDLINE, PsycINFO and SocINDEX (January 2000-December 2020). ELIGIBILITY CRITERIA: Empirical studies in English or French with a qualitative design including an explicit methodological description of the development and/or use of vignettes to collect qualitative data from healthcare professionals. Titles and abstracts were screened, and full text was reviewed by pairs of researchers according to inclusion/exclusion criteria. DATA EXTRACTION AND SYNTHESIS: Data extraction included study characteristics, definition, development and utilisation of a vignette, as well as strengths, limitations and recommendations from authors of the included articles. Systematic qualitative thematic analysis was performed, followed by data matrices to display the findings according to the scoping review questions. RESULTS: Ten articles were included. An explicit definition of vignettes was provided in only half the studies. Variations of the development process (steps, expert consultation and pretesting), data collection and analysis demonstrate opportunities for improvement in rigour and transparency of the whole research process. Most studies failed to address quality criteria of the wider qualitative design and to discuss study limitations. CONCLUSIONS: Vignette-based studies in qualitative research appear promising to deepen our understanding of sensitive and challenging situations lived by healthcare professionals. However, vignettes require conceptual clarification and robust methodological guidance so that researchers can systematically plan their study. Focusing on quality criteria of qualitative design can produce stronger evidence around measures that may help healthcare professionals reflect on and learn to cope with adversity.
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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 | 0.270 | 0.361 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.034 | 0.027 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".