Development and introduction of a comprehensive tobacco control policy in a Canadian regional health authority.
Bibliographic record
Abstract
BACKGROUND: Tobacco control policies in health care settings are necessary to protect patients, employees, physicians, visitors, and volunteers from the dangers of secondhand smoke. This report documents the process of developing and introducing a comprehensive tobacco control policy in one Canadian regional health authority. CONTEXT: Capital Health (CH), a health authority that has 30,000 employees and serves 1.6 million people, is responsible for 18 hospitals and primary care facilities, 33 continuing care facilities, 29 public health locations, and 9 community care facilities. CH recently determined that it needed to revise its tobacco control policy because its facilities had different directives regarding tobacco use, some of which did not reflect the best current knowledge about the health risks associated with exposure to secondhand smoke. METHODS: The new smoke-free policy needed to be developed and executed within a narrow time frame, which required careful planning as well as the support of patients and CH staff members. An essential part of the new policy was the prevention of nicotine withdrawal among people required to undergo involuntary tobacco abstinence. The plan also included an integrated screening, intervention, and referral process designed to optimize health benefits for patients and staff members who smoked, as well as for those who did not. CONSEQUENCES: CH decided to close all smoking rooms (including those in psychiatry, palliative care, geriatrics, eating disorder, and tuberculosis units), to ban smoking in outdoor areas, to stop all sales of tobacco products in CH facilities, to require smoke-free environments during home visitations, and to reject funding from the tobacco industry. INTERPRETATION: By implementing a consistent ban on indoor and outdoor smoking, CH is contributing to a comprehensive tobacco control policy that is arguably a regional health authority's most profound opportunity for health promotion.
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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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".