Qualitative methods and patient and public involvement in trials: opportunities and pitfalls
Bibliographic record
Abstract
Qualitative research and public patient involvement (PPI) in trials have increased over recent years, and can occur at many stages from inception to implementation. They offer different strengths and limitations, with both often needed to gain a real world perspective. Yet increasingly they are portrayed as a dichotomy in the way they are written about in grant applications, protocols and reports. How helpful is this? Qualitative research methods seek a deeper understanding of patient, health professional or other relevant perspectives on health-related conditions, or services within a wider social and cultural context. A trial is conceptualised as a unique social and cultural situation, where the intervention is just one event amongst many, often with both intended and unintended consequences. PPI is a philosophy of research being shaped by the people it is undertaken for and funded by, underpinned by the World Health Organisation, Ottawa Charter for Health Promotion 1986 and recent reforms in the UK Health and Social Care Act 2012. We explore this diagrammatically, to understand the contribution of each and the overlap where integration occurs. We will present a series of fallacies drawn from an analysis of the issues that we have observed as researchers, members of funding boards and in the literature. We consider the strengths and limitations for each approach, by asking why, what, where, who, when, and how? The question of how to assist trial researchers to find the best possible approach for their research questions is considered, including whether more prescriptive guidance is indicated.
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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 | 0.796 | 0.780 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.020 | 0.108 |
| Scholarly communication | 0.030 | 0.057 |
| Open science | 0.013 | 0.032 |
| Research integrity | 0.020 | 0.023 |
| Insufficient payload (model declined to judge) | 0.007 | 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".