Knowledge Synthesis of Smoking Cessation Among Employed and Unemployed Young Adults
Bibliographic record
Abstract
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.
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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 | 0.029 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.020 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".