Tobacco cessation counselling for women in rural Sindh: is it being offered?
Bibliographic record
Abstract
BACKGROUND: Tobacco is the single leading and most preventable cause of death in today's worlds and responsible for six of the eight leading mortality causes in the world. Diseases related to tobacco use are known to cause about 5.4 million deaths every year, 80% of which are contributed by the developing world, and this toll is estimated to increase up to 8 million deaths per year by 2030. This study was conducted to determine the number of women who were offered counselling regarding cessation of tobacco use by all health care providers (medical and alternate), in rural Sindh, Pakistan. METHODS: This cross-sectional survey was conducted during January to March, 2008 in District Khairpur, Sindh, Pakistan. A validated, pre-tested, translated questionnaire was used to collect the data from 502, adult women (aged between 18-60 years). These women were asked about the type of health provider they visited in the past 12 months and practices of provider regarding tobacco control including cessation and advice. RESULTS: A large majority of women (nearly 71%) were illiterate, and 44% of women were in the age group 18-24 years. High prevalence (10%) of adult women were smokers. Only 12% of the total women who visited physicians during this time period were asked about their smoking status as compared to 7% who visited hakims and 13% who were approached by lady health visitors. CONCLUSION: A very small segment of the women users of health care system is enquired and counselled about tobacco use in any form by the health providers in Rural Sindh. Revisiting practices for health care professionals is urgently needed to address inevitable tobacco use in the region.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".