Alignment and specificities of Brazilian health agencies with international premises for the implementation of digital health in Primary Health Care: a rhetorical analysis
Bibliographic record
Abstract
Abstract Objective To analyze the alignment of the arguments of Brazilian and international organizations for the adoption of digital health in Primary Health Care (PHC), from COVID-19. Methods This qualitative document analysis used a rhetorical analysis according to Perelman and Obrecht’s-Tyteca. Two independent researchers searched documents on the websites of the World Health Organization (WHO), the Pan-American Health Organization (PAHO), and the Brazilian Ministry of Health, Federal Council of Medicine, and Federal Council of Nursing between December 2021 and June 2022. The search terms were “digital health”, “telehealth”, “telemedicine”, “e-health”, “telessaúde”, “teleenfermagem”, “telemedicina", and “saúde digital”. Twenty official documents (recommendations, informative pages, guidelines, resolutions, laws, and ordinances) were identified, considering context, authorship, authenticity, reliability, nature, and key concepts. Results The international and Brazilian arguments emphasize the applicability of Information and Communication Technologies (ICTs) in health. In logical arguments based on the structure of reality, international bodies emphasize the overlap between health needs and the conditions for the applicability of ICTs, but in Brazil, there was a need to regulate the digital practices of these health professionals. In the structuring of the real, the international discourse contains illustrations of the relationship between the context of the health crisis caused by the COVID-19 pandemic and the concrete conditions for the applicability of digital health, while in Brazil the need to strengthen an environment conducive to the digital health policy. Conclusions The Brazilian alignment with international premises is evident, however, there is a need to strengthen the inclusion of digital health in the PHC policy in a socially and economically sustainable way.
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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 | 0.055 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.012 | 0.023 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| 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".