When all else fails: The (mis)use of qualitative research in the evaluation of complex interventions
Bibliographic record
Abstract
RATIONAL, AIMS, AND OBJECTIVES: Qualitative research has been promoted as an important component of the evaluation of complex interventions to support the scale up and spread of health service interventions, but is currently not being maximized in practice. We aim to identify and explore the sociocultural and structural factors that impact the uses (and misuses) of qualitative research in the evaluation of complex health services interventions. METHODS: We conducted a qualitative analysis of data collected in a multiple case study of the evaluation and scale up and spread of three health service intervention. RESULTS: Our findings demonstrate the challenges of meaningfully integrating qualitative research in evaluation programmes lead by clinicians with limited qualitative expertise and operating within an environment dominated by biomedical research, even with methodological support. CONCLUSIONS: Based on these findings we encourage ongoing engagement of qualitative researchers in evaluation programmes to begin to refine our methodological understanding, while also suggesting changes to medical education and evaluation funding models to create fertile environments for interdisciplinary collaborations.
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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 | 0.818 | 0.813 |
| Meta-epidemiology (narrow) | 0.002 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.018 | 0.120 |
| Scholarly communication | 0.032 | 0.048 |
| Open science | 0.010 | 0.027 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 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".