Bibliographic record
Abstract
This dissertation was developed as part of research to evaluate oral health services at the Primary Health Care (PHC) level, which created an evaluation matrix of indicators and digital tools for monitoring services using routine data from users' care in the public health system and made available in the Health Information System for Primary Care (SISAB). The scientific product is an article from a nationwide ecological study that analyzed morbidity due to toothache by population size in Brazilian municipalities (2018 to 2022). The indicator rate of dental care due to toothache (per 1000 users) was calculated for each Brazilian municipality, in 15 four months (1st quarter of 2018 to 3rd quarter of 2022) and according to population size (< 5 thousand; 5 to 9, 9 thousand; 10 to 49.9 thousand; 50 to 99.9 thousand; > 100 thousand inhabitants). Regression model for longitudinal data estimated the four-monthly variation over time by population size before and after the second quarter of 2020, as there was a pattern of rate reduction up to this point. Records obtained from 5,332 municipalities (95.72%) were analyzed. The average toothache attendance rate (per 1000 users) ranged from 9.65 (2nd quarter of 2020) to 27.24 (1st quarter of 2018). Throughout the period, larger municipalities received lower rates. Before the second quarter of 2020, the average rate in municipalities with >100 thousand inhabitants. presented an average quarterly reduction of 20.56%, greater than the 7.25% variation in our municipalities with < 5 thousand inhabitants. After the second quarter of 2020, the rate of municipalities < 5 thousand inhabitants. increased by 2.27%, a value lower than the variation in larger municipalities. On average, 10 to 30 consultations for toothache were provided in 4 months for 1000 registered users in Brazil, with the highest rates in smaller municipalities. There was a general reduction in rates until the second quarter of 2020, coinciding with the suspension of oral health services due to the COVID-19 pandemic, with this reduction being more pronounced in larger municipalities. After this period, rate growth was less anticipated, especially in small municipalities. The dissertation also presents the evaluation matrix systematized as a Dictionary of Indicators and the digital tools developed as a technical product. The dictionary describes the framework for developing the matrix and the qualification sheets for the 54 indicators measurable with SISAB data. The Indicator Panel for Monitoring Oral Health Services in PHC is an interactive tool with automated data collection, calculation of 54 indicators in a timely manner and demonstration of results by regions, Federation Units and Brazilian municipalities, through tables, graphs and maps, with geographic and temporal filters. The Oral Health Indicator Calculator sought to overcome the gap in providing indicators at the eSB level, using local management reports. The results of the rate of dental care for toothache in PHC indicate the persistence of this problem in public health services.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".