Contextualizing Smoking Behaviour over Time: A Smoking Journey from Pleasuring to Suffering
Bibliographic record
Abstract
This paper reports a qualitative study describing the basic psychosocial process of contextualizing smoking behaviour in the life fabric of Jordanian psychiatric nurses (JPNs). A classical grounded theory method was used to collect and analyze the data derived from a theoretical (purposeful) sample of eight Jordanian psychiatric nurses in 2009-2010. The constant comparative method of data analysis was used; thus, data collection, coding and analysis occurred simultaneously. Strategies were used throughout the study to ensure trustworthiness; that is, fulfill the requirements for credibility, transferability, dependability and confirmability. “Contextualizing smoking behaviour over time” was the core concept that explained how JPNs integrate smoking behaviour into their life fabric. For these nurses, smoking is contextualized in four phases: becoming a novice smoker, becoming a formal smoker as a nursing student, becoming a heavy smoking psychiatric nurse, and becoming an exhausted smoker. Contextualizing smoking among JPNs demonstrates that those nurses frequently normalize smoking as part of the fabric of everyday life. Participants described their smoking as a journey in a manner that reflected how it started with pleasuring and ended with suffering. Although this study presents a deep understanding of smoking behaviour, further studies are required to develop the theory of contextualized smoking. A developed contextualized theory of smoking is required to guide culturally sensitive smoking cessation and prevention programmes capable of influencing smoking behaviours
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".