Bridging Learning in Medicine and Citizenship During the COVID-19 Pandemic: A Telehealth-Based Case Study
Bibliographic record
Abstract
BACKGROUND: COVID-19 presented great challenges for not only those in the field of health care but also those undergoing medical training. The burden on health care services worldwide has limited the educational opportunities available for medical students due to social distancing requirements. OBJECTIVE: In this paper, we describe a strategy that combines telehealth and medical training to mitigate the adverse effects of the COVID-19 pandemic. METHODS: A toll-free telescreening service, Telecoronavirus, began operations in March 2020. This service was operated remotely by supervised medical students and was offered across all 417 municipalities (14.8 million inhabitants) in the Brazilian state of Bahia. Students recorded clinical and sociodemographic data by using a web-based application that was simultaneously accessed by medical volunteers for supervision purposes, as well as by state health authorities who conducted epidemiological surveillance and health management efforts. In parallel, students received up-to-date scientific information about COVID-19 via short educational videos prepared by professors. A continuously updated triage algorithm was conceived to provide consistent service. RESULTS: The program operated for approximately 4 months, engaging 1396 medical students and 133 physicians. In total, 111,965 individuals residing in 343 municipalities used this service. Almost 70,000 individuals were advised to stay at home, and they received guidance to avoid disease transmission, potentially contributing to localized reductions in the spread of COVID-19. Additionally, the program promoted citizenship education for medical students, who were engaged in a real-life opportunity to fight the pandemic within their own communities. The objectives of the education, organization, and assistance domains of the Telecoronavirus program were successfully achieved according to the results of a web-based post-project survey that assessed physicians' and students' perceptions. CONCLUSIONS: In a prolonged pandemic scenario, a combination of remote tools and medical supervision via telehealth services may constitute a useful strategy for maintaining social distancing measures while preserving some practical aspects of medical education. A low-cost tool such as the Telecoronavirus program could be especially valuable in resource-limited health care scenarios, in addition to offering support for epidemiological surveillance actions.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".