Alexitimia y estrés laboral en personal asistencial de un centro de salud del distrito de Comas, 2018
Bibliographic record
Abstract
A fin de determinar la relación entre alexitimia y estrés laboral en personal asistencial de un \ncentro de salud del distrito de Comas, 2018; se realizó una investigación la cual es de tipo \ncorrelacional, de corte transversal y su diseño es no experimental; donde la población estuvo \nconstituida por 93 colaboradores, entre ellos hombres y mujeres que se desempeñan en áreas \ndistintas de trabajo en un centro de salud, las cuales son; hospitalización, emergencia y \nconsultorio, siendo el total utilizado como muestra censal. Los instrumentos que se \nemplearon fueron: la escala de alexitimia de Toronto TAS-20 (1994) y la escala de estrés \nlaboral OIT-OMS (1984). Para determinar los resultados se emplearon los estadísticos: \ncoeficiente omega de McDonald, Kolmogorov – Smirnov (K-S), “rho” de Spearman, U de \nMann-Whitney para dos muestras independientes y la prueba de Kruskal-Wallis para varias \nmuestras con datos independientes; donde una de las conclusiones principales nos menciona \nque existe relación directa y significativa (p<0.05), entre alexitimia y las dimensiones de \nestrés laboral como son: clima organizacional, influencia de líder; se evidencia la relación \nentre territorio organizacional, falta de cohesión, estructura organizacional y tecnología.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".