[EM EDIÇÃO] Reestruturação de hospital geral para o enfrentamento a pandemia Covid-19: uma análise donabediana
Bibliographic record
Abstract
Objetivo: analisar a reestruturação de um hospital geral frente à pandemia de Covid-19 nos componentes “estrutura”, “processo” e “resultado” e suas relações com o ensino, a pesquisa e a assistência. Método: estudo realizado com 42 profissionais de hospital geral no Paraná, no formato caso único. Os dados foram operacionalizados pelo software MaxQda® seguindo as etapas da Análise de Conteúdo Temático Categorial à luz de Avedis Donabedian. Resultados: foram obtidas três categorias, representadas pelas respectivas subcategorias: Estrutura – recursos humanos, físicos; tecnológicos, materiais e insumos, financeiros e apoio externo; Processo, com protocolos, fluxos e dinâmica de atendimento, e atuação profissional; e Resultados, incluindo imediatos, mudanças no estado de saúde dos indivíduos, lições aprendidas, satisfação de expectativas dos trabalhadores e planejamento para o período pós-Covid-19. Considerações finais: diante da pandemia de Covid-19 como crise, a reestruturação do hospital permitiu atender a demanda sob olhar da estrutura, processo e resultado com maior relação assistencial quando comparado ao ensino e pesquisa.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".