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Record W2885404550 · doi:10.1186/s12889-018-5828-4

Two decades of tobacco use prevention and control policies in Cameroon: results from the analysis of non-communicable disease prevention policies in Africa

2018· article· en· W2885404550 on OpenAlexfundno aff
Clarisse Mapa-Tassou, Cécile Renée Bonono, Félix Assah, Jennifer P. Wisdom, Pamela A. Juma, Jean-Claude Katte, Zakariaou Njoumemi, Pierre Ongolo‐Zogo, Léopold Fezeu, Eugène Sobngwi, Jean Claude Mbanya

Bibliographic record

VenueBMC Public Health · 2018
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsTobacco controlPsychological interventionContext (archaeology)Public healthHealth policyHealth promotionTobacco industryEnvironmental healthMedicineNon-communicable diseasePublic policyGovernment (linguistics)Social marketingPromotion (chess)Public relationsEconomic growthPolitical sciencePoliticsEconomicsNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Tobacco use is the leading cause of preventable death in the world today. In 2010, the World Health Organization (WHO) proposed efficient and inexpensive "best buy" interventions for prevention of tobacco use including: tax increases, smoke-free indoor workplaces and public places, bans on tobacco advertising, promotion and sponsorship, and health information and warnings. This paper analyzes the extent to which tobacco use prevention policies in Cameroon align with the WHO tobacco "best buy" interventions. It further explores the context, content, formulation and implementation level of these policies. METHODS: This was a case study combining a structured review of 19 government policy documents related to tobacco use and prevention, in-depth interviews with 38 key stakeholders and field observations. The Walt and Gilson's policy analysis triangle was used to describe and interpret the context, content, processes and actors during the formulation and implementation of tobacco prevention and control policies. Direct observations ascertained the level of implementation of some selected policies. RESULTS: Twelve out of 19 policies for tobacco use and prevention address the WHO "best buy" interventions. Cameroon policy formulation was driven locally by the social context of non-communicable diseases, and globally by the adoption of the WHO Framework Convention on Tobacco Control. These policies incorporated at a certain level all four domains of tobacco use "best buy" interventions. Formulating policy on smoke-free areas was single-sector oriented, while determining tobacco taxes and health warnings was more complex utilizing multisectoral approaches. The main actors involved were ministerial departments of Health, Education, Finances, Communication and Social Affairs. The level of implementation varied widely from one policy to another and from one region to another. Political will, personal motivation and the existence of formal exchange platforms facilitated policy formulation and implementation, while poor resource allocation and lack of synergy constituted barriers. CONCLUSIONS: Despite actions made by the Government, there is no real political will to control tobacco use in Cameroon. Significant shortcomings still exist in developing and/or implementing comprehensive tobacco use and prevention policies. These findings highlight major gaps as well as opportunities that can be harnessed to improve tobacco control in Cameroon.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.165

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.006
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.100
GPT teacher head0.392
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations23
Published2018
Admission routes1
Has abstractyes

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