Evaluation of the stress level in professional health
Bibliographic record
Abstract
The quality of healthy attendance depends, among others factors, of the physical and mental state of the assistance professional. Researches directed to the health’s evaluation of these professionals are extremely important to guarantee the quality of the service. The purpose of this study was to evaluate the presence of stress among healthy professionals, the phase of stress in which they found themselves, the predominance of symptoms, physic and/or psychological, and to compare the level of stress between the professional categories. Thirty-one graduated professionals, who worked in a center of medical assistance (PAM), in a Rio de Janeiro’s quarter, participated in the research. They answered to ISSL and an Informative Questionnaire. Fifty-eight per cent of the participants were found stressed, and 94% of them had symptoms pertaining to the second phase of the stress process, the Resistance. In relation to the symptoms' prevalence, it was found that in 56% of the stressed professionals, there was predominance of physic symptoms. The categories of professionals with greatest prevalence of stress were from the social service, nursing and medicine areas. stress management procedures are suggested to guarantee a good level of services to the patients and a good quality of life to the professionals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".