Oral healthcare management practices in Brazil
Bibliographic record
Abstract
Universal health coverage is a global target included in the United Nations Sustainable Development Goals agenda for 2030. Healthcare in Brazil has universal coverage through the Unified Health System (SUS), which guarantees health as basic right to the Brazilian population. Considering the principles of SUS, public oral healthcare management is a huge challenge. Aim: To identify good management practices for quality care adopted by local public oral healthcare managers and teams around Brazil. Methods: This study was registered with PROSPERO (CRD42017051639). Five databases (PubMed, Embase, Web of Science, Scopus and Lilacs) as well as the reference lists and citations of the included publications were searched according to PRISMA guidelines. Results: A total of 30,895 references were initially found, which were evaluated according to the defined eligibility criteria. Twenty qualitative studies, eight surveys and two mixed-model studies were selected. The practices (codes) were organized into three main groups (families), and the Frequency of the Effect Size (FES) of each code was calculated. Among the 20 codes identified, the most relevant ones were: Diagnosis and Health Planning (FES=80%) and Family Health Strategy (FES=66,7). The Intensity of the Effect Size of each study was also calculated to demonstrate the individual contribution of each study to the conclusions. Conclusion: The evidence emerging from this review showed that healthcare diagnosis, planning, and performance based on the family health strategy principles were the most relevant practices adopted by public oral healthcare managers in Brazil. The widespread adoption of these practices could lead to improved oral healthcare provision and management in Brazil.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".