The integration of tobacco reduction activities into the practice of acute care registered nurses : a mixed methods research project
Bibliographic record
Abstract
In light of evidence linking tobacco use with various health issues, hospitals have become viable contexts for tobacco control strategies. Clinicians, in particular nurses, are being challenged to address patients' tobacco use by providing cessation interventions. Emerging evidence indicates that nurses support the idea of encouraging people to stop smoking, perceive an expectation upon them to address patients' tobacco use, and are reluctant to approach patients beyond assessing smoking status. Following this lead a concurrent mixed methods research project investigated acute care registered nurses' integration of tobacco reduction interventions. All nurses working at two hospitals in British Columbia, Canada were included in the project; hospitals were situated in regions that represented diversity in population smoking rates (19.6%- 31.2%). Two hundred and fourteen nurses (58% response rate) participated by completing a survey and ethnographic data collection was completed on the 16 adult inpatient wards, which included 135 hours of field observations, 114 brief conversations, document collection, and photographs of designated smoking areas. This first Canadian investigation of acute care nurses revealed similar views and practice activities related to tobacco use interventions. Beyond assessing smoking status, less than half of the respondents reported consistently advising, assisting, or arranging referrals for patients. Site comparisons demonstrated differences in available tobacco related resources, as well as reported practices of assisting and arranging referrals. Testing of a path model, which hypothesized causal mechanisms influencing nurses' practice, demonstrated nearly half of the variance in nurses' uptake of intervention activities was explained by the role attitude and four measures of perceived barriers. Indirect relationships were noted from perceived tobacco workplace climate factors and smoking status. The ethnographic profile of tobacco use and control in the study sites suggested systemic devaluing concerning support for patients' cessation efforts, a lack of awareness of addiction issues related to tobacco, and several burdens that patients' tobacco use brings to nurses' practice. Findings from each study were integrated into a multi-dimensional ecological behavior system, which emphasized the importance of moving beyond focusing on clinicians as a means to change practice. Rather health care institutions will be required to implement system-wide tobacco control strategies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".