Innovations in the practice of Brazilian community health nursing during the pandemic: a rapid review
Bibliographic record
Abstract
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 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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".