Healthcare providers’ level of involvement in provision of smoking cessation interventions in public health facilities in Kenya
Bibliographic record
Abstract
Healthcare providers can play a major role in tobacco control by providing smoking cessation interventions to smoking patients. The objective of this study was to establish healthcare providers' practices regarding smoking cessation interventions in selected health facilities in Kiambu County, Kenya. This was a descriptive cross-sectional study carried out among healthcare providers working in public health facilities in Kiambu County, Kenya. Self-administered questionnaires were distributed to 400 healthcare providers selected using a two-stage stratified sampling technique. Only 35% of the healthcare providers surveyed reported that they always asked patients about their smoking status. Less than half (44%) reported that they always advised smoking patients to quit. Respondents who had received training on smoking cessation interventions were 3.7 times more likely to have higher practice scores than those without training (OR = 3.66; 95%CI: 1.63-8.26; P = 0.003). Majority of the healthcare providers do not routinely provide smoking cessation interventions to their patients. Measures are needed to increase health worker's involvement in provision of smoking cessation care in Kenya.
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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 | 0.001 | 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) | 0.002 | 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".