Costos de riesgos laborales y los ingresos netos de rentas cuarta-quinta categoría que perciben las enfermeras en el Hospital del distrito Sicuani 2016.
Bibliographic record
Abstract
The present research, entitled "LABOR RISK COSTS AND NET INCOME INCOME FOR FOURTH QUARTER CATEGORY PERCEIVED BY NURSES IN THE HOSPITAL OF THE SICUANI-2016 DISTRICT" whose research was carried out with the purpose of describing the extent to which Occupational risk costs affect the fourth-fifth-grade net income earned by nurses in the Sicuani District hospital, who are scant remunerative scales and do not compensate for assigned work and that the hospital is a hazardous environment that is exposed To occupational hazards workers in the health sector that this context is the nursing staff. \n \nThe present study has the following scope: Descriptive, analytical, with non experimental design and quantitative approach. \nThe techniques of data collection used have been, the documentary analysis, the survey and interview. \n \nThe development of the theoretical framework has started from the identification of the variables and the consequent deep revision of bibliographical sources that guarantee the proper sustenance in the present investigation. \n \nThe population of informants consisted of a total population of 50 nurses; That we \nhad to select a part of the population that is the sample of 24 nurses who declare not to use social security for different reasons, which were surveyed.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".