The Opinion of Employees and Children Living in Foster Care Homes About Romanian National Clean Air Legislation on Tobacco Smoking
Bibliographic record
Abstract
Abstract This study aims to assess the awareness and opinions of employees and children living in foster care homes about the Romanian Clean Air Legislation. The assessment was performed six months after the implementation of the antitobacco legislation (Romanian Law no.15/2016), in three Romanian counties (Alba, Mures, and Covasna) including 178 employees and 368 children from 59 foster care homes. Data were collected using an anonymous paper and pencil questionnaire. Descriptive statistics and chi-square test were used for data analysis (significant difference if p < 0.05). Almost one third of the employees and children were smokers from the forest care homes. Nearly, all the employees and most of the children were aware of the legislation, most of them have also noticed some kind of measures taken by foster care homes against smoking. As claimed by smokers, almost two-thirds of them smoked like they used to do before the legislation, more than a quarter stated that they decreased the number of daily smoked cigarettes and nearly a quarter intended to quit smoking in the future. A half year after the implementation of the legislation most of the employees and children living in foster care homes admitted that they knew about the change and it affected their smoking habits. Most of them have also observed some kind of measures taken against smoking. Despite these measures there were still smokers in the foster care homes, so there would be a need for more campaigns against smoking in the future.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".