Creating spaces for care for nurses working in the pandemic in light of the nursing process
Bibliographic record
Abstract
OBJECTIVES: To make a dialog about the nursing professionals' perception regarding how they cope with COVID-19 and the repercussions on their practice and personal life. METHODS: This is a qualitative study, typified as participatory action research, which was carried out using Paulo Freire's Research Itinerary linked to the steps of the Nursing Process. To that end, the following guiding question was launched: How is it for you to act as a nursing professional in the hospital area during the COVID-19 pandemic? RESULTS: Three syntheses emerged, which guided the discussion: The challenges of being a nursing professional in the pandemic. The learning and growth that the challenges of the pandemic have generated and Nursing as the protagonist of care. The Virtual Culture Circle was a space where, despite the limitations, provided a social interaction among the participants, with mutual exchange of experiences, with many reflections, besides expressions of feelings, experiences and learning obtained during the COVID-19 pandemic. CONCLUSIONS: The nurses perceived that, although this moment highlights and appreciates the profession, nursing is overloaded and exhausted by the COVID-19 pandemic, with repercussions on professional and personal life. The care for those who care needs to be planned and implemented in different scenarios, and the Nursing Process built based on theoretical and scientific knowledge guide the effective improvement of the quality of health care.
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.027 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.005 | 0.005 |
| 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".