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Record W2122165981 · doi:10.1136/tc.2007.022368

A cost-effectiveness analysis of a community pharmacist-based smoking cessation programme in Thailand

2008· article· en· W2122165981 on OpenAlexaff
Kednapa Thavorn, Nathorn Chaiyakunapruk

Bibliographic record

VenueTobacco Control · 2008
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSmoking cessationPharmacistCommunity pharmacistFamily medicineMedicineEnvironmental healthBusinessPharmacy

Abstract

fetched live from OpenAlex

OBJECTIVE: To estimate the incremental cost-effectiveness ratio of a structured community pharmacist-based smoking cessation programme compared with usual care. DESIGN: A cost-effectiveness study using a healthcare system perspective Population: Two simulated cohorts of smokers: male and female aged 40, 50 and 60 years who regularly smoke 10-20 cigarettes per day. Intervention and comparator: A structured community pharmacist-based smoking cessation (CPSC) programme compared to usual care. MAIN OUTCOME MEASURE: Cost per life year gained (LYG) attributable to the smoking cessation programme. RESULTS: The CPSC programme results in cost savings of 17,503.53 baht ( pound250; euro325; $500) to the health system and life year gains of 0.18 years for men and; costs savings of 21,499.75 baht ( pound307; euro399; $614) and life year gains of 0.24 years for women. A series of sensitivity analyses demonstrate that both cost savings and life year gains are sensitive to variations in the discount rate and the long-term smoking quit rate associated with the intervention. CONCLUSION: From the perspective of the health system, the CPSC programme yields cost savings and life year gains. This finding provides important information for health policy decision-makers when determining the magnitude of resources to be allocated to smoking cessation service in community pharmacy.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.011
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.116
GPT teacher head0.366
Teacher spread0.250 · 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 teacher head, 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

Citations56
Published2008
Admission routes1
Has abstractyes

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