Bibliographic record
Abstract
The high rates of tobacco use among individuals with schizophrenia are well documented, but there has been less attention paid to identifying what are the special needs for this population. In fact, there have even been suggestions from early work that standard interventions and approaches might be adequate. In contrast, based on more than a decade of experience supporting change smoking behavior among people with schizophrenia, three key factors were identified as unique considerations that are associated with success. The first factor involves readiness to change; smokers with schizophrenia are rarely given opportunities to even try to quit unlike their counterparts in the general population and therefore have not benefited from the self-efficacy aspects of attempt experiences. The second factor is medication and symptom monitoring; there are special needs for nurses and medical staff to monitor symptoms (including schizophrenia symptoms and mood symptoms), medication dosage and side-effects, during the period when individuals with schizophrenia are changing (reducing) their tobacco use, particularly when nicotine replacement therapy is being implemented. Finally, the third factor is peer and caregiver support; the use of peer assistants in group-based programs and the teaching of nurses and other professional casegivers as well as family members about their role as supports can make an important difference in tipping the balance toward successful change and toward maintenance of change over time.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".