Assessment of empathy and psychological characteristics of smokers
Bibliographic record
Abstract
Introduction Smoking causes many life-threatening diseases and is a major risk factor for several main causes of death. Damage by smoking is also caused to those called “passive smokers”. Passive smoking has been implicated in many adverse effects on health. Very few studies deal with empathy in smokers while smoking in the presence of non-smokers. Purpose Estimation of empathy and psychological characteristics of Greek smokers. Methodology For research purposes we used questionnaires Toronto empathy questionnaire (TEQ), symptom check list 90-R, experience of shame scale (EES) and other as Shamer scale – OAS. Results A statistically significant difference has been observed in the TEQ to gender (2.436, 0.02), with women showing the highest value (3.4 ± 0.4), as well as in the mean in behavioral shame 2. Another statistically significant difference has been observed in behavioral shame between educational level (3.419, 0.026) and the price of characterological shame among those who reported smoking in the workplace and those who did not. Conclusions The sample consisted of 27 subjects with average age 24 years and standard deviation of 4 years. Smokers accounted for 48.1%, of whom 69.2% stated that smoke in their living space. Respectively, the percentage of smokers in the workplace was equivalent. The largest percentage of smokers, (13 people, 69.2%) think that non-smokers are very annoyed when surrounded by people smoking close to them. The proportion of those who said they are very much bothered when in places where smoking is prohibited but somebody smokes, is large (47.1%). Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".