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Record W2337575139 · doi:10.1186/1745-6215-16-s2-p75

Qualitative methods and patient and public involvement in trials: opportunities and pitfalls

2015· article· en· W2337575139 on OpenAlexaboutno aff
Pat Hoddinott, Alicia O’Cathain, Isabel Boyer, Sandy Oliver

Bibliographic record

VenueTrials · 2015
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsnot available
FundersMedical Research Council
KeywordsMedicineQualitative researchAlternative medicineClinical trialPublic involvementFamily medicineMedical educationPathologyPublic relationsSocial science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.061
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.291
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0610.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.943
GPT teacher head0.679
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2015
Admission routes1
Has abstractyes

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