Bibliographic record
Abstract
Background In recent years, tobacco use in the Czech Republic has slightly declined, however, approximately a quarter of the population aged 15 and over are smokers. Pharmacists are one of the most easily accessible healthcare professionals and can, therefore, play a key role in the process of smoking cessation. Objectives Primary objective of this work was mapping the smoking cessation support available in Czech pharmacies. Secondary aims included assessing the interest of pharmacists to perform smoking cessation interventions and the importance of different factors hindering them from doing so. Other objectives were mapping different types of smoking cessation support provided in pharmacies and opportunity to refer them to other healthcare professionals. Methods A quantitative survey study was used. Questionnaire were compiled in online platform Survio. Data were collected in March 2022. The questionnaire was distributed via email using the list of registered pharmacies on SÚKL website. In total, 2681 facilities were approached, of which 132 responded. One questionnaire was invalid, therefore, final selection was comprised of 131 pharmacies, the response rate was 4.92 %. Acquired data were processed and analysed in MS Excel software. Results From all respondents, 80.2 % provide or are interested...
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".