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Record W1992777892 · doi:10.1503/cmaj.140545

How to assess a survey report: a guide for readers and peer reviewers

2015· review· en· W1992777892 on OpenAlexafffundvenue
Karen E. A. Burns, Michelle E. Kho

Bibliographic record

VenueCanadian Medical Association Journal · 2015
Typereview
Languageen
FieldSocial Sciences
TopicSurvey Methodology and Nonresponse
Canadian institutionsMcMaster UniversitySt. Joseph’s Healthcare HamiltonSt. Michael's Hospital
FundersCanadian Institutes of Health Research
KeywordsComputer scienceSurvey researchReading (process)Peer reviewData scienceWorld Wide WebInformation retrievalPsychologyApplied psychology

Abstract

fetched live from OpenAlex

Although designing and conducting surveys may appear straightforward, there are important factors to consider when reading and reviewing survey research. Several guides exist on how to design and report surveys, but few guides exist to assist readers and peer reviewers in appraising survey methods.[

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)0.190
metaresearch head score (Gemma)0.496
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: Review · Consensus signal: none
Teacher disagreement score0.810
Threshold uncertainty score0.999

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1900.496
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0170.015
Science and technology studies0.0030.003
Scholarly communication0.0060.008
Open science0.0050.003
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0390.058

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 head0.478
GPT teacher head0.522
Teacher spread0.043 · 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
GenreReview

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

Citations121
Published2015
Admission routes3
Has abstractyes

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