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Innovations in the practice of Brazilian community health nursing during the pandemic: a rapid review

2024· review· en· W4403607450 on OpenAlexaff
Margareth Santos Zanchetta, Clarissa Moura de Paula, Katarinne Lima Moraes, Walterlânia Silva Santos, Francisca Márcia Pereira Linhares, Lizete Malagoni de Almeida Cavalcante Oliveira, Virgínia Visconde Brasil, Alecssandra de Fátima Silva Viduedo

Bibliographic record

VenueEscola Anna Nery · 2024
Typereview
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPandemicMedicineNursingCoronavirus disease 2019 (COVID-19)Nursing practiceDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Abstract Objective to describe the innovations introduced in Brazilian community health nursing during the COVID-19 pandemic. Method rapid literature review with a conceptual framework of innovation in organizations and ideas underlying the method of appreciative inquiry. The databases were limited to Google Scholar (English) and SciELO (Portuguese) (November–December 2022). A total of 52 articles were identified in Portuguese, 11 of which met the eligibility criteria. Results the majority (n=10; 91%) addressed the “discovery” factor, highlighting the favorable conditions for innovation. Contents about “imagining what could be” (n=6; 55%) projected innovation as a permanent practice. In relation to “co-constructing the ideal condition” 55% (n=6), innovation was reported as a joint action and in practice. Regarding the “sustainability of innovation,” only five interventions (45%) indicated paths for continuity. Conclusions and implication to the practice the connection between primary health care, academia, and organizations produced simple solutions to unknown, complex, and unpredictable situations. However, the idea of innovation as something unprecedented, untested, and structurally revolutionary, was not extensively identified by this rapid review, due to the conceptual and theoretical fragility of the interventions and projects reported.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.878
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.211
GPT teacher head0.544
Teacher spread0.333 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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