Opportunities to Improve the Role of Family Practice Nurses in Increasing the Uptake of Evidence-Based Smoking Cessation Interventions for Pregnant Women: An Exploratory Survey
Bibliographic record
Abstract
Background: Approximately 6-30% of Canadian women smoke during pregnancy. Prenatal care visits are an opportune time for Family Practice Nurses to provide evidence-based smoking cessation interventions. The purpose of this exploratory study was to describe: 1) Smoking cessation interventions by Family Practice Nurses during prenatal visits; 2) Family Practice Nurses' awareness and use of smoking cessation guidelines as well as the proportion of Family Practice Nurses who engage pregnant women who smoke in minimal interventions and intensive interventions; 3) the predictors of nurse-provided smoking cessation counseling for pregnant women and 4) the barriers to smoking cessation counseling. Methods: A previously validated questionnaire measuring smoking cessation counseling practices was modified and converted to an electronic format. A bilingual invitation was emailed to the members of Ontario Family Practice Nurses' interest group of the Registered Nurses' Association of Ontario, Canada. Descriptive and multivariate analyses were completed. Predictors investigated included nurses' age, beliefs about their role in smoking cessation, self-efficacy to provide effective counseling, smoking cessation training, and interest in updating smoking cessation knowledge. Eighty-nine Family Practice Nurses working in primary care settings across Ontario, Canada responded. Results: Nurses with higher levels of self-efficacy were more likely to provide smoking cessation counseling. Although nurses Ask, Advise and Assess, they are less likely to provide concrete assistance in the quitting process or arrange follow-up. The most commonly cited barriers to nurse-provided smoking cessation counseling included cost of medication, lack of time, and lack of knowledge. Fourteen percent of respondents reported that they never offer smoking cessation counseling to pregnant women. Conclusions: Family Practice Nurses are not consistently providing evidence-based smoking cessation interventions for pregnant women. Disseminating research outlining effective strategies to increase nurses' selfefficacy to provide effective smoking cessation interventions may increase the uptake of evidence-based recommendations.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".