Modification of a Smoking Motivation Questionnaire for Chinese Medical Students
Bibliographic record
Abstract
INTRODUCTION: Smoking prevalence among the medical students is high in China. Therefore, understanding the smoking motivations of medical students is crucial for smoking control, but currently there are no scales questionnaires customized for probing the smoking motivations of medical students. This aim of study was to test and modify a questionnaire for investigating smoking motivations among medical students. METHODS: A cross-sectional survey was conducted among 1,125 medical students at Xuzhou Medical College in China in 2012.The model fit and validity was assessed by confirmatory factor analysis (CFA) and the reliability was tested by single-item reliability, composite reliability, and item-total correlation. RESULTS: The prevalence of smoking was 9.84 % among study population. In the modified scales, the global fit indices identified a CFI value of 0.96, TLI was 0.96, and the RMSEA was 0.063. CFA supported the two dimensional structure of the instrument. The average variance extracted ranged from 0.45 to 0.62. All single-item reliability scores were greater than 0.20, and the composite reliability ranged from 0.74 to 0.91. CONCLUSION: Modified scales could be the preliminary instrument used in evaluating the smoking motivations of medical students. However, it should be further assessed using other forms and methods of validity and reliability, additional motivations of smoking, and the survey of other medical colleges in China.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".