Bibliographic record
Abstract
Tobacco use is one of the most preventable sources of death and disease, and yet remains a worldwide problem. With the rising costs of healthcare, the focus of efforts to control them has honed in on lifestyle behaviors that contribute to the escalating costs. Within the scope of this scrutiny, the prevention or cessation of smoking and tobacco usage has become a global priority and a major focal point of worldwide anti-tobacco initiatives. The World Health Organization (WHO) has identified cessation interventions by health care professionals as a crucial factor in successful patient smoking cessation, and studies have shown that personal smoking behaviors by health care professionals are a barrier to effective smoking cessation interventions by those professionals (Lally et al., 2008; Radsma & Bottorff, 2009). This knowledge fueled the creation and distribution of global surveys by the WHO, the Centers for Disease Control and Prevention (CDC), and the Canadian Public Health Association (CPHA) to investigate the prevalence of smoking behaviors in health care professionals and in health care students (“Global Health Professions,” 2014; “WHO/CDC Global,” 2014). This study utilized the Global Health Professions Student Survey. The current study investigated the prevalence of smoking in nursing students of all educational levels at East Tennessee State University, with the expectation that the percentage of students who currently smoke would be substantially lower than that of students who do not smoke. The survey also investigated attitudes towards the role of health professionals in patient smoking cessation and towards personal smoking behaviors.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".