To assess the preparedness of primary care physicians in terms of their knowledge, attitude, beliefs, and confidence regarding smoking cessation in Gilgit
Bibliographic record
Abstract
Background: Tobacco smoking poses a great threat to the healthcare system both in developed and developing countries due to its health hazard. Healthcare professionals and in particular physicians can play an effective role in encouraging people to quit smoking which will ultimately improve the overall health of their patients and hence prolong their lives. Subjects and Methods: Data were collected from physicians from public and private hospitals that included Aga khan Health Services in Gilgit, Ghizer, and Hunza, District health quarter Hospitals in Gilgit, Hunza, and Ghizer, Sehat foundation between 1 December, 2021, and 30 May, 2022. A precoded questionnaire was filled which assessed the knowledge, attitude, practices, beliefs, and confidence of physicians in smoking cessation. Results: value 0.002). 55.5% (n = 57) were unsure that Bupropion helps in quitting smoking and only 23.5% (n = 24) reported that they are very well prepared for counselling, whereas 42.2% (n = 44) were unsure how to assess smoker's different stages of readiness to quit. Conclusion: We concluded that physicians of Gilgit Baltistan have sound knowledge about the adverse effects of smoking, but they are not confident in prescribing medication due to unaware of different methods of treatment available for smoking cessation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".