Systematic Approaches for Telemedicine and Data Coordination for COVID-19 in Baja California, Mexico
Bibliographic record
Abstract
Background In 2019, the State of Baja California had a total population of 3,682,063 inhabitants; in the city of Tijuana, there were only 13 Red Cross ambulances and 1 fire department ambulance to attend to the prehospital emergencies of almost 2 million inhabitants. Objective This study aimed to provide information to the public; evaluate COVID-19 in real time; and track regional, municipal, and state-wide data in real time that inform supply chains and resource allocation with the anticipation of a surge in COVID-19 cases. Methods Our model is based on human-centric design factors and cross-disciplinary collaborations for the scalable, data-driven enablement of smartphone teleconsultation technologies to link hospitals, clinics, and emergency medical services for point-of-care assessments of COVID-19 testing and subsequent treatment and quarantine decisions. Results The Telehealth System handled 28,964 telephone calls in the period from April 1, 2020, to January 30, 2022, and accumulated 20,287 working hours. In total, 13,721 follow-up calls were made to quarantined patients, providing medical and psychological counseling, and 12,643 calls were received and transferred from the 911 system, of which 4964 calls from patients with respiratory symptoms required urgent ambulance dispatch. Conclusions Telehealth offers capabilities for remote detection, care, and treatment to help with supervision, surveillance, discovery, and prevention, as well as to mitigate the effects of health care indirectly related to COVID-19.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".