Entrenamiento a los profesionales del centro de salud “Fuerte Militar Marco Aurelio Subía” en técnicas cognitivo conductuales para gestionar el estrés presente y prevenir el Síndrome de Burnout en el período de marzo – julio del 2020
Bibliographic record
Abstract
La presente sistematización extrae experiencias respecto a la elaboración de una guía de talleres con técnicas cognitivo-conductuales que permita trabajar en los niveles altos de estrés y prevenir el síndrome de Burnout que se evidenció en los/as profesionales del Centro de Salud “Fuerte Militar Marco Aurelio Subía”. \nComo eje de la sistematización se hizo énfasis en las acciones ejecutadas por parte de la autora a lo largo de la elaboración de la guía. \nEntre las técnicas planteadas en la guía se destacaron: las técnicas de relajación como: relajación diferencial variante Labrador, relajación autógena de Schultz, técnicas de relajación grounding y selfholding; reestructuración cognitiva y entrenamiento en habilidades sociales. \nFinalmente surgieron preguntas clave a las cuales posteriormente se dio respuesta, mediante el contraste comparativo de elementos de la experiencia (sesiones, tareas, dinámicas grupales, ejercicios, etc.) que mostraron importantes tensiones productivas entre sí o diferenciación notable entre elementos que mostraban resultados o características buscadas o deseadas y elementos que no.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".