Barriers to Providing Smoking Cessation Intervention by Nursing Students: What is the Solution in Nursing Education?
Bibliographic record
Abstract
AIM: This study aimed to explore the barriers that hinder nursing students from providing comprehensive smoking cessation interventions for their clients. METHOD: A mixed method study combining a self-administered questionnaire and one open-ended question were used to collect data from 152 nursing students at the university in Canada. Data were analyzed using descriptive statistics and thematic analysis. The Health Belief Model was the theoretical underpinning for this study. RESULTS: Participants showed positive attitudes toward smoking cessation interventions as being a part of their future work. However, students faced many barriers that hindered them from providing smoking cessation interventions to their clients. The participants identified the following seven themes/barriers: the lack of knowledge, training, resources, and time; the willingness of patients to quit; lack of students' self-confidence; students' level of comfort; smoking cessation being covered by other members of the health care team; patients already being knowledgeable about smoking cessation; and protecting therapeutic relationships with patients. CONCLUSION: There is a need for empowering nursing students and enhancing their self-confidence in smoking cessation interventions by incorporating theory-based educational materials and strategies regarding smoking cessation interventions in their curricula.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| 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".