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Record W3164212433 · doi:10.1177/10497323211016408

Experimental (Re)structuring: The Clinical Trial as Turning Point Among Medical Research Participants

2021· article· en· W3164212433 on OpenAlexafffund
Kaitlyn Jaffe, P. Todd Korthuis, Lindsey Richardson

Bibliographic record

VenueQualitative Health Research · 2021
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsBritish Columbia Centre on Substance UseUniversity of British Columbia
FundersCanadian Institutes of Health ResearchNational Institute on Drug AbuseCanada Research Chairs
KeywordsRandomized controlled trialThematic analysisQualitative researchAddictionContext (archaeology)PsychologySocioeconomic statusPopulationMedicinePsychiatrySociologySocial scienceEnvironmental healthGeography

Abstract

fetched live from OpenAlex

Amid the growth of addiction medicine randomized controlled trials (RCTs), scholars have begun examining participants' study experiences, highlighting facilitators and barriers to enrollment. However, this work can overlook the interplay between trial participation and social-structural dimensions among people with substance use disorders linked to the social nature of use, socioeconomic marginalization, and time demands of substance procurement and use. To effectively conduct RCTs with this unique population, it is necessary to examine the broader social context of study participation. We conducted nested qualitative interviews with 22 participants involved in an RCT testing a treatment for alcohol and opioid use disorders in HIV clinics. Thematic analyses revealed social-structural circumstances shaping RCT participation as well as how participation constitutes a turning point, prompting individuals to reconfigure social networks, reorient to spatial environments, and reorganize day-to-day life-with implications for how substance use disorder RCTs should be approached by researchers.

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)0.187
metaresearch head score (Gemma)0.023
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.165
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1870.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0080.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.010
Insufficient payload (model declined to judge)0.0100.002

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.

Opus teacher head0.949
GPT teacher head0.790
Teacher spread0.159 · 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; both teacher heads agree on what is shown here.

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

Citations8
Published2021
Admission routes2
Has abstractyes

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