Tobacco use and COVID-19 in Ghana: generating evidence to support policy and practice
Bibliographic record
Abstract
Background The COVID-19 pandemic has affected over 45 million people and caused over a million deaths globally. Tobacco use, a threat to public health worldwide, increases the risk of developing severe COVID-19 disease and death. The hand-to-mouth action, smoking-induced lung diseases, and the sharing of tobacco products such as water pipes, increase a smoker’s vulnerability to COVID-19. Implementation of tobacco control efforts in low- and middle-income countries (LMICs) including sub-Saharan Africa (SSA) is a particular challenge. The aim of this study in Ghana was to produce evidence to support governments to make informed policy decisions about tobacco control interventions in the context of COVID-19. Methods A survey with key stakeholders (conducted online or via face to face interview) and a desk-based mapping of data sources including government reports and online print press. Face-to Face interviews followed the COVID-19 precautionary protocols. Results 40 stakeholders participated in the interviews (28 online and 12 face-to-face). 46 data sources were identified from the mapping of which 16 were eligible for data extraction. Over two fifths of survey respondents (42.9%, n=12) agreed that the relationship between COVID-19 and tobacco use had been discussed in the media, and over half (57%, n=16) reported that public health professionals and other authorities had provided advice to tobacco users during the pandemic. While respondents (89%, n=25) did not see a change in the level of interest in tobacco cessation, less than a quarter (23%, n=6) indicated that the policy response to COVID-19 included a focus on tobacco control issues, but was limited to tobacco cessation. The majority of respondents (77%, n=31) reported a perceived limited impact on the tobacco industry’s operations during the pandemic. Conclusions COVID-19 provides a timely opportunity to strengthen tobacco control efforts by recognizing the role of tobacco use in potentially exacerbating covid-19 health outcomes and promoting 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 | 0.065 | 0.186 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".