The effects of cognitive and emotional status on smoking cessation.
Bibliographic record
Abstract
OBJECTIVE: Smoking cessation is affected by multiple factors including cognitive status of the patients. In this study, we aimed to investigate the effects of demographic, emotional and cognitive functions of 39 male and 42 female patients who applied to the smoking cessation outpatient clinic on smoking cessation. PATIENTS AND METHODS: This study recruited 81 healthy volunteers of equal age, gender, and educational level. Total Montreal Cognitive Assessment (MoCA) scores were compared according to age, gender, cessation methods, and Beck Depression Inventory and Beck Anxiety Inventory (BAI) scores in smoking cessation settings. RESULTS: In our study, there were 39 (48.1%) male patients and 42 (51.9%) female patients. While 36 patients were able to quit smoking, the remaining 38 were unable to do so. During follow-up, 7 patients had yet to be reached. Age, years of smoking, number of cigarettes smoked per day, education level, first reason for starting smoking, reasons for quitting smoking, quitting method, and medical drugs used were found to have no effect on smoking cessation; however, the MoCA total score, Beck depression scale, Beck anxiety scale, and smoking cessation scale score were found to have significant effects on smoking cessation. CONCLUSIONS: Various cognitive processes, particularly visuospatial and attention skills, have been found to be useful in quitting smoking. Furthermore, emotional states, such as depression and anxiety have a negative impact on quitting smoking. We believe that if it is provided to the patients in the smoking cessation outpatient clinic to boost cognitive capabilities and treat mood problems, the success of smoking cessation will increase.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".