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Record W2951103683 · doi:10.1177/1359105319855120

Strengthening behavioral clinical trials with online qualitative research methods

2019· article· en· W2951103683 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Health Psychology · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicFocus Groups and Qualitative Methods
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsQualitative researchAffordanceClinical trialIntervention (counseling)Psychological interventionPsychologyTranslational researchBehavioural sciencesApplied psychologyResearch designMedicineClinical psychologyPsychotherapistPsychiatryCognitive psychology

Abstract

fetched live from OpenAlex

Qualitative methods are integral to the systematic development of effective health behavioral interventions, as noted in recent translational models, such as the Obesity-Related Behavioral Intervention Trials model. To our knowledge, however, no scholarly literature describes how online qualitative research methods can be used to benefit the development and conduct of behavioral clinical trials. We emphasize the value of qualitative methodologies to behavioral clinical trial research more broadly and provide an introductory overview of online qualitative research methods. We highlight the specific affordances of these methods in maximizing the quality of behavioral clinical trials as well as note their potential limitations. Finally, we argue that online qualitative research methods ought to be incorporated in behavioral trial development and call for future research in this area.

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)0.437
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.772
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.4370.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.857
Teacher spread0.057 · 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