Salud mental de las enfermeras luego de la pandemia por el COVID 19
Bibliographic record
Abstract
Methods: This was a descriptive, cross-sectional study, through an integrative review on the mental \nhealth of nurses after the COVID 19 pandemic. For this, a search was carried out in the PubMed \ndatabase, with the keywords “nurse's mental health” “Covid 19” NOT “health workers”, published \nin the first quarter of 2021 and that had full text display. \nResults: The search yielded a total of 125 articles, as a result of which 7 were selected for full \nanalysis. The mental disorders with the highest incidence in nurses who worked with patients \ncarrying Covid 19 were depression, anguish, exhaustion, anxiety and stress, as well as posttraumatic stress disorder. \nConclusion: The results of this study clearly demonstrated that the prevalence of stress, anxiety \nand depression among nurses who cared for patients with COVID-19 was high. Therefore, the \nimportance of creating interventions and strategies that can alleviate these mental health disorders \nthat may have appeared in your health workers..
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".