Improving tobacco cessation interventions in hospitals: Pre–post evaluation of an innovative health systems intervention in Catalonia (Spain)
Bibliographic record
Abstract
This study measured changes in healthcare professionals' (HCPs) performance in tobacco cessation intervention before and 6 months after a health system intervention. The intervention involved exposure to online training for staff and the implementation of a structured organizational change-level practice model that included some strategies, comprising establishing tobacco cessation steering groups with champions in each hospital, developing tailored protocols and guidelines within each organization, conducting on-site workshops for clinicians, and creating posters and pocket materials summarizing the intervention. Pre-post evaluation in four hospitals in Barcelona province (Catalonia, Spain). We assessed the knowledge, attitudes, behaviors, and organizational factors (KABO) and the performance of each of the components of the 5As Model for Treating Tobacco Use according to a scale from 0 ("Never") to 10 ("Always") among HCPs. We performed Wilcoxon signed-rank tests for paired samples and assessed changes in performance by performing linear regression. A total of 255 HCPs completed the pre-post evaluation. All components of the 5As Model increased, with "Assist" and "Arrange a follow-up" showing the greatest improvement. Several KABO dimensions significantly increased, including individual skills (mean score: 3.3-5.7, P < .001), attitudes and beliefs (4.8-5.4, P < .001), individual commitment (5.9-6.6, P < .001), and perception of having positive organizational support (4.3-4.7, P < .001). An increase in each point in individual skills and support of the organization was associated with increased rates of 5As delivery, with the greatest associations found for "Assist" (0.60 and 0.17, respectively) and "Arrange a follow-up" (0.71 and 0.18, respectively). The intervention was successful in increasing HCPs individual skills, attitudes and beliefs, individual commitment, and perception of having positive organizational support and the performance of all components of the 5As. Future research should focus on strategies that promote organizational support, a dimension that is essential to increasing Assist and Arrange, which were less implemented at baseline.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".