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Record W1962433605 · doi:10.4137/sart.s960

The Influence of Web- versus Paper-Based Formats on the Assessment of Tobacco Dependence: Evaluating the Measurement Invariance of the Dimensions of Tobacco Dependence Scale

2009· article· en· W1962433605 on OpenAlexaff
Chris G. Richardson, Joy L. Johnson, Pamela A. Ratner, Bruno D. Zumbo

Bibliographic record

VenueSubstance Abuse Research and Treatment · 2009
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsCentre for Advancing Health OutcomesUniversity of British Columbia
Fundersnot available
KeywordsEquivalence (formal languages)Web surveyThe InternetMeasurement invarianceComprehensionPsychologyScale (ratio)StatisticsComputer scienceMathematicsClinical psychologyWorld Wide WebStructural equation modelingConfirmatory factor analysisGeography

Abstract

fetched live from OpenAlex

The purpose of this study was to examine the influence of mode of administration (internet-based, web survey format versus pencil-and-paper format) on responses to the Dimensions of Tobacco Dependence Scale (DTDS). Responses from 1,484 adolescents that reported using tobacco (mean age 16 years) were examined; 354 (23.9%) participants completed a web-based version and 1,130 (76.1%) completed a paper-based version of the survey. Both surveys were completed in supervised classroom environments. Use of the web-based format was associated with significantly shorter completion times and a small but statistically significant increase in the number of missing responses. Tests of measurement invariance indicated that using a web-based mode of administration did not influence the psychometric functioning of the DTDS. There were no significant differences between the web- and paper-based groups' ratings of the survey's length, their question comprehension, and their response accuracy. Overall, the results of the study support the equivalence of scores obtained from web- and paper-based versions of the DTDS in secondary school settings.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score0.355

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.157
GPT teacher head0.415
Teacher spread0.257 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2009
Admission routes1
Has abstractyes

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