Healthcare providers’ perspectives on implementing a brief physical activity and diet intervention within a primary care smoking cessation program: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Post-smoking-cessation weight gain can be a major barrier to quitting smoking; however, adding behavior change interventions for physical activity (PA) and diet may adversely affect smoking cessation outcomes. The "Picking up the PACE (Promoting and Accelerating Change through Empowerment)" study assessed change in PA, fruit/vegetable consumption, and smoking cessation by providing a clinical decision support system for healthcare providers to utilize at the intake appointment, and found no significant change in PA, fruits/vegetable consumption, or smoking cessation. The objective of this qualitative study was to explore the factors affecting the implementation of the intervention and contextualize the quantitative results. METHODS: Twenty-five semi-structured interviews were conducted with healthcare providers, using questions based on the National Implementation Research Network's Hexagon Tool. The data were analyzed using the framework's standard analysis approach. RESULTS: Most healthcare providers reported a need to address PA and fruit/vegetable consumption in patients trying to quit smoking, and several acknowledged that the intervention was a good fit since exercise and diet could improve smoking cessation outcomes. However, many healthcare providers mentioned the need to explain the fit to the patients. Social determinants of health (e.g., low income, food insecurity) were brought up as barriers to the implementation of the intervention by a majority of healthcare providers. Most healthcare providers recognized training as a facilitator to the implementation, but time was mentioned as a barrier by many of healthcare providers. Majority of healthcare providers mentioned allied health professionals (e.g., dieticians, physiotherapists) supported the implementation of the PACE intervention. However, most healthcare providers reported a need for individualized approach and adaptation of the intervention based on the patients' needs when implementing the intervention. The COVID-19 pandemic was found to impact the implementation of the PACE intervention based on the Hexagon Tool indicators. CONCLUSION: There appears to be a need to utilize a flexible approach when addressing PA and fruit/vegetable consumption within a smoking cessation program, based on the context of clinic, the patients' it is serving, and their life circumstances. Healthcare providers need support and external resources to implement this particular intervention. NAME OF THE REGISTRY: Clinicaltrials.gov. TRIAL REGISTRATION NUMBER: NCT04223336. DATE OF REGISTRATION: 7 January 2020 Retrospectively registered. URL OF TRIAL REGISTRY RECORD: https://classic. CLINICALTRIALS: gov/ct2/show/NCT04223336 .
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 | 0.027 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".