Health Care Professionals Implementing a Smoke-Free Policy at Inpatient Psychiatric Units
Bibliographic record
Abstract
Smoke-free grounds policies (SFGPs) were introduced to inpatient psychiatric hospital settings to improve health among patients, staff, and visitors. We conducted an ethnographic study in Northern British Columbia, Canada, to describe how the implementation of SFGPs is affected by institutional cultures. Data reported here included participant observation, document review, informal discussions (n = 11), and interviews with health care professionals (HCPs; n = 19) and staff (n = 2) at two hospitals. We used iterative and inductive processes to derive thematic findings. Findings related to HCPs illustrate how local contexts and cultural factors affect SFGP implementation. These factors included individual beliefs and attitudes, the influence of group norms, leadership and consensus building, and locale-specific norms. Strong, consultative leadership, in which leaders solicited input from and long-term support of people most directly responsible for policy implementation, was key to success.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".