“Boy, what are we all doing? We are crazy, really crazy”: a qualitative study of psychosocial processes around an atypical one-time smoking cessation course
Bibliographic record
Abstract
BACKGROUND: Smoking prevalence is still high, which requires effective interventions that help many people who smoke at once in addition to time-consuming individual interventions. 'I Quit' is a large-scale smoking cessation course in The Netherlands. This qualitative study explored I Quit participants' experiences during and after the course, and perceptions of whether and how the course may have altered their smoking behavior. METHODS: We performed individual semi-structured interviews with course participants (N = 21) who had either quit successfully, attempted to quit but relapsed, or had continued to smoke after 'I Quit'. Shortly after qualitative data collection was completed, Foundation I Quit was accused in the media of a number of misbehaviors. Although unplanned, this provided a unique opportunity to explore participants' views on alleged fraud in a second round of interviews (N = 16). Data were collected from 2016 to 2018. RESULTS: Qualitative findings showed two psychosocial processes that may explain smoking cessation after course attendance. First, the confrontation with a large group of people who smoke, of whom some had already developed smoking-related complaints, triggered identity processes both towards and away from quitting smoking. Unorthodox methods used in the course appeared to trigger identity processes. Second, social support after the course from participants' own social network facilitated maintenance of successful quitting. The study also found that interview participants' opinions on I Quit did not change much after allegations of fraud in the media. CONCLUSIONS: Findings suggest that a one-time course might initiate psychosocial processes that could help certain smokers to gain motivation to quit, requiring a minimum of resources. Identity processes triggered by the course seem tricky as people have different ways of dealing with identity threat, some of which can be counterproductive and even result in more difficulty quitting. More research is needed to examine who can benefit from a one-time course, and who needs more support in order to quit successfully.
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 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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".