[A tool to evaluate hospital nursing practices toward smoking cessation].
Bibliographic record
Abstract
UNLABELLED: Nurses in hospitals are not active in smoking cessation because of certain beliefs and attitudes. Beliefs and attitudes must be measured for changing practice in quitting smoking. The objective is to develop and validate a questionnaire on smoking cessation practices of nurses in hospitals. METHODOLOGY: A methodological study was conducted to construct a questionnaire (n = 118) according to the theory of planned behaviour, to validate by four experts, for reliability and validation of instruments constructs (n = 38; n = 29; n = 157). RESULTS: An initial questionnaire on practices in smoking cessation was built according to the beliefs of a convenience sample of 118 nurses. Validation of experts was conducted, and the questionnaire obtained an index of content validation (ICV) of 0.94. Subsequently, after two convenience samples (n = 38; n = 29) and a random sample (n = 157), the questionnaire obtained reliability, measured by Cronbach's alpha ranging in 0.697 and 0.93 1. Finally, moderately high correlations (0.406 to 0.569) were obtained between concepts. CONCLUSION: A reliable and valid questionnaire in French is available to measure smoking cessation practices.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".