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Record W4230222798 · doi:10.1177/1049731513498118

Developing a Reporting Guideline for Social and Psychological Intervention Trials

2013· article· en· W4230222798 on OpenAlexaff
Sean Grant, Paul Montgomery, Sally Hopewell, Geraldine Macdonald, David Moher, Evan Mayo‐Wilson

Bibliographic record

VenueResearch on Social Work Practice · 2013
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsOttawa Hospital
FundersEconomic and Social Research CouncilMedical Research CouncilClarendon FundUniversity of OxfordUniversity College London
KeywordsPsychological interventionRandomized controlled trialConsolidated Standards of Reporting TrialsGuidelineIntervention (counseling)PsychologyAlternative medicineQuality (philosophy)Medical educationApplied psychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

Social and psychological interventions are often complex. Understanding randomized controlled trials (RCTs) of these complex interventions requires a detailed description of the interventions tested and the methods used to evaluate them; however, RCT reports often omit, or inadequately report, this information. Incomplete and inaccurate reporting hinders the optimal use of research, wastes resources, and fails to meet ethical obligations to research participants and consumers. In this article, we explain how reporting guidelines have improved the quality of reports in medicine and describe the ongoing development of a new reporting guideline for RCTs: Consolidated Standards of Reporting Trials-SPI (an extension for social and psychological interventions). We invite readers to participate in the project by visiting our website, in order to help us reach the best-informed consensus on these guidelines ( http://tinyurl.com/CONSORT-study ).

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.661
metaresearch head score (Gemma)0.801
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.339
Threshold uncertainty score0.418

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.6610.801
Meta-epidemiology (narrow)0.0080.012
Meta-epidemiology (broad)0.0170.033
Bibliometrics0.0280.030
Science and technology studies0.0050.011
Scholarly communication0.0180.016
Open science0.0180.009
Research integrity0.0290.041
Insufficient payload (model declined to judge)0.0110.018

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 head0.982
GPT teacher head0.863
Teacher spread0.118 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainReporting
GenreMethods

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

Citations28
Published2013
Admission routes1
Has abstractyes

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