Impacto do trabalho e satisfação da equipe multiprofissional atuante em um hospital psiquiátrico
Bibliographic record
Abstract
Health is the result of proper management concerning physical, emotional , social, professional, intellectual and spiritual areas of ones life, and the quality of life of the working man or woman is relates to maintaining in equilibrium, given the situations of everyday life .The hospital adds to the employers working environment very specific aspects of workload, direct contact with extreme situations, and high level of tension and high risks for themselves and others.This study aimed to identify job satisfaction and the job's impact on professional workers from a multidisciplinary team in a Psychiatric Hospital.An exploratory, descriptive study, with a quantitative cross-sectional approach was conducted in the city of São Paulo with the multidisciplinary team of a Psychiatric Hospital.The study population was composed of 136 members of the multidisciplinary team of the Psychiatric Hospital, totaling 116 participants.Three instruments were used for data collection: Roadmap for interview subjects (REPSM), Scale for Assessment of Satisfaction on he for Mental Health Services Team (SATIS -BR abbreviated) Rating Scale of Labor Impact inside Mental Health Services (IMPACT -BR).Ethical approaches were respected.We conducted a descriptive analysis based on inferential statistical analysis.The results show that of the 116 study participants, 63.8 % were female, with an average age of 37.5 years, 54 % were single, 41.9 % lived with a partner and 51.7 % did not have children.In the range between 1 and 5, the average satisfaction score was 3.4, in which satisfaction concerning "Relationship" occurred in greater number, and in lower number concerning the factor: "Participation in the service".The average score concerning overload was 1.9, in respect to "Effects of the job upon the relationship inside the team" and lesser on "Effects on physical and mental health".We observed a negative correlation between satisfaction and overload, highlighting as such the number of employees, the necessity for specific training of staff and the possibility of greater participation in decisions during service.Conclusion: Low levels of overload and satisfaction show the need for revision of institutional projects to improve the quality of life of workers and assistants.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".