An impact evaluation of the Ontario ban on smoking in schools and on school property : does the ban on smoking influence high school students' intentions to smoke cigarettes?
Bibliographic record
Abstract
The Ontario Tobacco Control Act of 1994 imposed a total ban on smoking in \nschools, and on school property for every school in the province. The imposition of this \npolicy created problems for school administrators. For instance, students who were \nsmoking on walkways and properties adjacent to school boundaries, clashed with \nneighbouring property owners who were angry about the resulting damage and \ndisruption. \nThe enforcement of this policy consumes valuable resources at each school; \ntherefore, knowledge about the impact of the policy is important. If effective, this policy \nhas the potential to improve the health of students over their lifetime, by preventing or \ndelaying smoking behaviour. Alternatively, an ineffective policy will continue to create \nadministrative problems for the school and serve no legitimate purpose. Therefore, \nknowledge about the impact of the smoking ban policy on students' smoking intentions \nassists policy makers and school administrators in their understanding of the policy's \nimpact within the schools. \nThis research provided an impact evaluation of the ban on smoking in schools and \non school property in Ontario. A total of 2069 students, from five high schools, in the \nNiagara Region, provided complete responses to a survey, designed to test whether \nsmoking intentions were affected by the imposition of the policy. The study used \nAjzen's theory of planned behaviour (Ajzen, 1991), specifically, the perceived \nbehavioural control measure, to gain some understanding of students' perceptions of \ncontrol over smoking imposed by the ban. \nThe findings indicate the policy has the potential to influence students' overall \nsmoking intentions. The ban on smoking policy was found to be a significant predictor \nof the smoking intentions of high school students. As well, attitude, social norms, and \nperceptions of control were significant predictors of smoking intentions. Exploratory \nfindings also indicated differences between the control beliefs of students from different \nhigh schools, indicating potential differences in the enforcement of the smoking ban \nbetween schools. \nThe findings also support the utility of the theory of planned behaviour as a \nmethodology for evaluating the influence of punitive policies. This research study should \nbe continued by utilizing the full theory of planned behaviour, including two phases of \ndata collection and the measurement of actual smoking behaviour.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".