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Record W2131013717 · doi:10.2105/ajph.2006.100909

Knowledge Synthesis of Smoking Cessation Among Employed and Unemployed Young Adults

2007· review· en· W2131013717 on OpenAlexaff
Pearl Bader, Heather E. Travis, Harvey A. Skinner

Bibliographic record

VenueAmerican Journal of Public Health · 2007
Typereview
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsToronto Public Health
Fundersnot available
KeywordsDelphi methodSmoking cessationPsychological interventionFocus groupYoung adultPopulationMedicineOptimismPsychologyGerontologyFamily medicineEnvironmental healthPsychiatrySocial psychologySociology

Abstract

fetched live from OpenAlex

OBJECTIVES: We synthesized evidence regarding effective strategies for smoking cessation among employed or unemployed young adults aged 18 to 24 years. METHODS: For this knowledge synthesis, we used (1) a systematic review of the scientific literature, (2) a Delphi panel of experts, and (3) 6 focus groups of employed and unemployed young adult smokers. RESULTS: Of 51 related studies, only 4 included employed and unemployed young adults in their samples (as opposed to students), and none focused solely on them. Using the Delphi process, 27 experts reached consensus on priorities for research, practice, and policy, emphasizing population engagement, recruitment, and innovative interventions. Key themes from focus groups were that interventions should be relevant to young adults, individual choice should be respected, and the positive aspects of quitting should be stressed. Despite having negative views on traditional smoking cessation methods, participants expressed optimism about being able to quit and proposed creative recommendations. CONCLUSIONS: Our findings set an agenda for targeting research, improving practice, and informing policy for smoking cessation among young adults. We also demonstrate the value of using 3 complementary approaches: literature review, expert opinion, and target population perspectives.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.922
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.107
GPT teacher head0.409
Teacher spread0.302 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations45
Published2007
Admission routes1
Has abstractyes

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