Alignment and specifics of Brazilian health agencies in relation to the international premises for the implementation of digital health in primary health care: a rhetorical analysis
Bibliographic record
Abstract
Digital health and sustainable development goals have had strong impacts with the COVID-19 pandemic. In Brazil, the health crisis scenario required changes in social welfare programs and policies, based on recommendations from international agencies, such as the UN and WHO. This study aims to analyze the alignment of the arguments of Brazilian and international organizations for the adoption of digital health in Primary Health Care based on the COVID-19 pandemic. This is a qualitative documentary study of the rhetorical analysis type, based on Perelman and Obrechts-Tyteca's Theory of Argumentation. The search for documents was carried out by two independent researchers, between December 2021 and June 2022, through the websites of the World Health Organization, the Pan American Health Organization, the Brazilian Ministry of Health, and the Federal Councils of Medicine and Brazilian nursing, with the terms "digital health," "telehealth," "telemedicine," "e-health," "telehealth," "telenursing," "telemedicine," and "digital health." Twenty official documents were analyzed and identified in terms of context, authorship, authenticity, reliability, nature, and key concepts. The international and Brazilian arguments emphasize the applicability of Information and Communication Technologies (ICTs) in the health field. In logical arguments, based on the structure of reality, international agencies emphasize the overlap between health needs and the conditions for the applicability of ICTs. In Brazil, however, there was a need to regulate the digital practices of health professionals. In the international discourse, in the structuring of reality, there are illustrations of the relationship between the context of the health crisis caused by COVID-19 and the concrete conditions for the applicability of digital health; while in the Brazilian discourse, the need to strengthen an environment conducive to digital health is explicit. The Brazilian alignment in relation to the international premises is evident. Yet, there is a need, socially and economically sustainable, to strengthen the inclusion of digital health in PHC policy.
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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 | 0.032 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.013 | 0.022 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| 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".