Psychometric Properties of the Autonomy over Tobacco Scale in German
Bibliographic record
Abstract
BACKGROUND/AIMS: We investigated the psychometric properties of a German translation of the 12-item Autonomy over Tobacco Scale (AUTOS) among 1,195 eighth-grade students. METHODS: Data for this study were collected as part of the fourth wave of data collection of the Smokefree Class Competition intervention in the Saxony-Anhalt region of Germany. Students from the control arm of the Smokefree Class Competition study who indicated that they had ever smoked 'at least a few puffs' on a cigarette were classified as ever-smokers. They self-completed questionnaires distributed by teachers. RESULTS: AUTOS scores ranged from 0 to 36 with a distribution highly skewed toward lower-response categories. Inter-item correlations ranged from 0.65 to 0.89 (mean = 0.79, SD = 0.06). Composite reliability for the AUTOS was high (Ω = 0.96) and 3 lower-order factors were also reliable (withdrawal: 0.89, psychological dependence: 0.91, cue-induced cravings: 0.87). Concurrent validity was supported by strong relationships between the AUTOS and both lifetime cigarette consumption and current smoking frequency. Youths were 18 times more likely to be current smokers (95% CI = 11.9-27.2, p < 0.001) if they endorsed any AUTOS item. CONCLUSION: The German AUTOS is reliable and valid, and the results are consistent with the English AUTOS for use with adolescents.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".