Mental health in the age of COVID-19, a Mexican experience
Bibliographic record
Abstract
As of June 2020 the number of Coronavirus cases in Canada, Mexico, Central America and the Caribbean are just under 2.5 million infections and over 140,000 deaths. The health systems in half of the countries in the Americas and the rest of the world have faced the pandemic positioned from different perspectives. While Canada and the United States already had extensive experience in the practice of telemedicine, other countries such as Mexico and the Caribbean, doctors from both private and public sectors have been forced to start practicing medicine remotely. As a result there have been limitations such as poor access to technology, lack of privacy legislation, and difficulties with fee collection among many others. These situations must be taken in account to understand what is happening in the region. On the other hand, the need to continue providing medical attention is indisputable. We understand that COVID 19 besides other systems damages the CNS, patients present severe neuropsychiatric symptoms that range from headache, anosmia, ageusia, confusional state alteration of consciousness, toxic metabolic encephalopathies, encephalitis, seizures, cerebral vascular events, Guillan Barre-type demyelinating neuropathies, to the extent of conditions such as anxiety, acute stress disorder, post-traumatic stress disorder, depression, and eventually psychotic episodes. As time passes we try to differentiate the origin of the symptoms. We will learn which of these symptoms are a result of metabolic complications, which others are due to drug's secondary effects and which ones are adaptive response. Therefor our contribution to the editorial supplements is given in two lines of analysis: disease physiopathology and ways to deliver treatment to the population.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".