Evaluacion de un programa de deshabituación tabáquica en personal sanitario
Bibliographic record
Abstract
Objetivos: El objetivo del trabajo es evaluar el exito de un programa de deshabituacion tabaquica en personal hospitalario en terminos de abandono del habito tras 12 meses de seguimiento. Identificar posibles factores predictores de exito de la terapia. Diseno. Cohorte retrospectiva. Ambito y sujetos de estudio. Trabajadores del Complexo Hospitalario de Ourense que participaron en el programa de deshabituacion tabaquica desde 1998 a 2004. Analisis. Se realizo un analisis descriptivo de las variables y un analisis de regresion logistica donde la variable dependiente es la situacion del fumador tras 12 meses de seguimiento. El resto de variables se incluyeron en el modelo como independientes. Hallazgos. Entre 1998 y 2004 fueron atendidos en nuestra consulta 263 trabajadores, 70% mujeres. No completaron el seguimiento a los 12 meses 48 (18%). El colectivo atendido mas numeroso fue enfermeria (35%). El 16% nunca habia intentado dejar de fumar. De los 215 trabajadores que completaron el seguimiento seguian sin fumar tras 12 meses el 25%, no habiendo diferencias significativas en el porcentaje de exitos en funcion del sexo. Conclusiones. Nuestro programa logro un 25% de abstinencia a los 12 meses de seguimiento, porcentaje similar al encontrado en otros estudios. No encontramos variables que puedan predecir el exito de la terapia de deshabituacion tabaquica.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".