Bibliographic record
Abstract
Smoke-free campus policies at inpatient health facilities are most effective when situated within comprehensive smoking cessation programs that include cessation support for staff and patients and effective communications and signage for staff, patients, and visitors. Canadian jurisdictions such as Ontario, New Brunswick, Prince Edward Island, Alberta, British Columbia, and Northwest Territories have provincial smoke-free legislation that applies to the grounds of health facilities. This approach permits public health inspectors and peace officers to enforce the smoke-free grounds rules with the option of issuing fines to individuals or hospital corporations for non-compliance. There is very little existing evidence on the effectiveness of issuing fines as a means of enforcing smoke-free policies. There can be unique considerations associated with implementing smoke-free policies in inpatient psychiatric facilities or units, given the relationship between mental health and substance use issues and tobacco use. Evidence shows that smoke-free policies are feasible and result in positive health outcomes in psychiatric facilities or units. Staff may require additional education and training in smoking cessation and tools to support productive conversations with patients, visitors, or colleagues who are not in compliance with smoke-free policies. Examples of tools and communications materials used in other jurisdictions are provided in Appendix 1.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.147 | 0.023 |
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".