Psychometric Properties of Instruments That Measure Vaping Outcome Expectancies: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: Vaping is a growing public health concern. Interventions that address vaping must build upon rigorous research that uses psychometrically sound instruments to measure vaping-associated outcome expectancies. AIMS AND METHODS: The primary aim was to appraise the reporting of psychometric properties of instruments used to measure vaping outcome expectancies. Secondary aims were to distinguish the different types of outcome expectancies assessed across the measures, the conceptual underpinnings, and the evidence explaining e-cigarette use etiology. This systematic review was guided by an adapted version of the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) guideline and Risk of Bias Checklist. Five electronic databases were searched for peer-reviewed studies, dissertations, and theses that psychometrically evaluated instruments that measure vaping outcome expectancies. Studies that met the inclusion criteria were appraised based on their reporting of nine psychometric properties outlined in the COSMIN checklist. RESULTS: The review included 11 studies that described eight instruments and reported on two to five of nine predetermined psychometric properties. Structural validity, construct validity, and internal consistency were the most commonly reported properties. No studies reported test-retest, intrarater, or interrater reliability, measurement error, or responsiveness. Content validity and measurement invariance were only reported by two and four studies, respectively. The most commonly included subscales in the instruments were affect regulation, positive sensory experience, and negative health consequences. Many of the outcome expectancy subscales were associated with e-cigarette behaviors. CONCLUSIONS: There is limited reporting of psychometric testing of instruments that measure vaping outcome expectancies; however, utilization of the COSMIN guideline could enhance the quality of such reporting. IMPLICATIONS: Appraising the reporting of psychometric properties of instruments that measure vaping outcome expectancies is a first step to ensuring valid and reliable instruments are used to support rigorous research and build evidence-based knowledge. Future research should focus on testing for responsiveness, measurement error, and reliability, and on quality appraisal of the instruments. Studying vaping outcome expectancies may improve understanding of factors that influence and deter vaping. This may contribute to the development of effective interventions aimed at vaping cessation and prevention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.061 | 0.270 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".