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Record W3035611753 · doi:10.1186/s12874-020-01029-4

Improving design choices in Delphi studies in medicine: the case of an exemplary physician multi-round panel study with 100% response

2020· article· en· W3035611753 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueBMC Medical Research Methodology · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsnot available
FundersNewfoundland and LabradorAchmea
KeywordsDelphi methodDelphiOperations researchComputer sciencePsychologyMedical educationMedicineManagement scienceData scienceArtificial intelligence

Abstract

fetched live from OpenAlex

BACKGROUND: A proper application of the Delphi technique is essential for obtaining valid research results. Medical researchers regularly use Delphi studies, but reports often lack detailed information on methodology and controlled feedback: in the medical literature, papers focusing on Delphi methodology issues are rare. Since the introduction of electronic surveys, details on response times remain scarce. We aim to bridge a number of gaps by providing a real world example covering methodological choices and response times in detail. METHODS: The objective of our e(lectronic)-Delphi study was to determine minimum standards for emergency departments (EDs) in the Netherlands. We opted for a two-part design with explicit decision rules. Part 1 focused on gathering and defining items; Part 2 addressed the main research question using an online survey tool. A two-person consensus rule was applied throughout: even after consensus on specific items was reached, panellists could reopen the discussion as long as at least two panellists argued similarly. Per round, the number of reminders sent and individual response times were noted. We also recorded the methodological considerations and evaluations made by the research team prior to as well as during the study. RESULTS: The study was performed in eight rounds and an additional confirmation round. Response rates were 100% in all rounds, resulting in 100% consensus in Part 1 and 96% consensus in Part 2. Our decision rules proved to be stable and easily applicable. Items with negative advice required more rounds before consensus was reached. Response delays were mostly due to late starts, but once panellists started, they nearly always finished the questionnaire on the same day. Reminders often yielded rapid responses. Intra-individual differences in response time were large, but quick responders remained quick. CONCLUSIONS: We advise those considering Delphi study to follow the CREDES guideline, consider a two-part design, invest in personal commitment of the panellists, set clear decision rules, use a consistent lay-out and send out your reminders early. Adopting this overall approach may assist researchers in future Delphi studies and may help to improve the quality of Delphi designs in terms of improved rigor and higher response rates.

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.

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)0.214
metaresearch head score (Gemma)0.356
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.286
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.2140.356
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.008
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.002
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 head0.914
GPT teacher head0.690
Teacher spread0.225 · 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