Change and Continuity in Vaping and Smoking by Young People: A Qualitative Case Study of a Friendship Group
Bibliographic record
Abstract
This paper explores as a case study the development of e-cigarette use and smoking within small friendship group (n=8) in Glasgow, Scotland. Interviewed twice at six months apart these 16/17 year olds reported substantial change in their use of and attitudes towards e-cigarettes and tobacco. At time 1 vaping generated much excitement and interest, with 6/8 having their own vape device. At time 2 only two young people still vaped, with the others no longer professing any interest in continued vaping. The two regular smokers, who had been smoking before they first vaped, now only vaped privately and to reduce their tobacco intake. This small case study illustrates plasticity in the use of electronic cigarettes; just as young people can initiate using these devices so too can they more away from their use- with such changes in actual use occurring within a relatively short period of time. These findings demonstrate more than anything else the volatility in young peoples’ substance use behaviour. If we are to better understand these behaviours we require both quantitative and qualitative research studies that are capable of both monitoring changes in individual and group behaviour over time but which are also able to elucidate the nuance of individual behaviour differentiating between long term, frequent, consistent use and more episodic, experimental and infrequent use by young people.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".