MétaCan
Menu
← Back to cohort
Record W2801702920 · doi:10.3389/fpubh.2018.00149

Best Practices for Developing and Validating Scales for Health, Social, and Behavioral Research: A Primer

2018· review· en· W2801702920 on OpenAlexaff
Godfred O. Boateng, Torsten B. Neilands, Edward A. Frongillo, Hugo Melgar‐Quiñonez, Sera L. Young

Bibliographic record

VenueFrontiers in Public Health · 2018
Typereview
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsMcGill University
FundersNational Institute of Mental Health
KeywordsScale (ratio)JargonResource (disambiguation)Best practiceComputer scienceProcess (computing)Reliability (semiconductor)Data scienceRating scaleManagement sciencePsychologyEngineering

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.374
metaresearch head score (Gemma)0.431
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.626
Threshold uncertainty score0.771

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3740.431
Meta-epidemiology (narrow)0.0050.005
Meta-epidemiology (broad)0.0040.007
Bibliometrics0.0230.016
Science and technology studies0.0050.017
Scholarly communication0.0160.032
Open science0.0100.013
Research integrity0.0120.024
Insufficient payload (model declined to judge)0.0040.005

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 head0.962
GPT teacher head0.795
Teacher spread0.167 · 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
DomainMethods
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

Citations5,204
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in Public Health→Same topicHealth Policy Implementation Science→French-language works237,207→