Respiratory Therapists' Smoking Cessation Counseling Practices: A Comparison Between 2005 and 2010
Bibliographic record
Abstract
OBJECTIVE: We assessed whether smoking cessation counseling practices and related psychosocial characteristics among respiratory therapists (RTs) improved between 2005 and 2010. METHODS: Data were collected in mailed self-report questionnaires in 2005 and in 2010, in random independent samples of active licensed RTs in Québec, Canada. RESULTS: The response proportion was 67.6% in 2005 and 59.9% in 2010. There were no substantial differences in mean cessation counseling scores according to year of survey. RTs who reported that they had received cessation counseling training during their studies or after their studies (when they were in practice) had statistically significantly better counseling practices for both patients ready and patients not ready to quit than untrained RTs. In addition, their self-efficacy to provide effective counseling was higher and they perceived fewer knowledge-related barriers to cessation. Further, RTs trained after their studies perceived fewer patient-related and time barriers to cessation counseling, and had better knowledge of community resources. CONCLUSIONS: Although the proportion of RTs trained in smoking cessation counseling during and after studies increased between 2005 and 2010 (from 3% to 14%, and from 17% to 29%, respectively), sustained efforts are needed to increase the number of trained RTs, so that this translates into positive observable changes in counseling practices.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".