The Frequency of Smoking and Common Factors Leading to Continuation of Smoking among Health Care Providers in Tertiary Care Hospitals of Karachi
Bibliographic record
Abstract
BACKGROUND: The primary objective of the study was to find out the frequency of tobacco smoking among health care providers in tertiary care hospitals of Karachi. The secondary objective was to identify the common factors responsible for the continuation of smoking. METHOD: This cross sectional study was conducted in the wards and out-patient departments of three selected tertiary hospitals of Karachi. A total of 180 health care providers were enrolled in the study using proportionate stratified sampling. Postgraduate students, house officers and trainees were excluded from the study. Data were collected from randomly selected health care providers using survey methodology. SPSS v. 20.0 was used to enter and analyze the data. RESULTS: Fifty two participants out of 180 were smokers for past one year (28.9%). Among them, 21 (11.7%) smoked more than 5 cigarettes per day. Twenty smokers (11.1%) were found to smoke due to peer influence. It was found that those who were influenced by their peers were 8.33 times more prone to be addicted to smoking than those who were less influenced. Similarly, the likelihood of addiction increased up to 76.9% with the lack of incentives. CONCLUSION: Our results clearly indicate that a large number of health care providers smoke which should be a serious concern. Hence our health agencies should take immediate action in order to curtail the heaving burden of smoking and its related health consequences.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".