Impact of COVID-19 on Vulnerable Groups: A Need for Mental Health Facilities
Bibliographic record
Abstract
The latest challenge for the universe is Novel Coronavirus disease 2019 (COVID-2019). Although it is not new for the entire medical world this recent outbreak is new in humans. It started in Wuhan, China through animal to human spread but later on it was evidenced as human to human spread. On January 30, WHO declared Public health emergency around the world but did not impose trade and travel restrictions. Following China, On 19 February Iran spoke about 2 deaths due to COVID-19. Pakistan shares its border with China and Iran and has trade and travel relations with both countries. So this virus was imported through travelers and 1st case was reported on 26 February in Pakistan (Health, 2020). Until today number of cases has been outstretched up to 28,736 while 636 deaths were reported (Worldometer, 2020). All these current scenarios, call for attention to the impact of this pandemic on mental health. When large numbers of people get sick or die as a result of epidemics or pandemics, it leads to greater risks for psychosocial problems. History reminds us that SARS was the 1st hard hit of the 21st Century and researchers reported the huge psychosocial impact of SARS upon people (Sim & Chua, 2004). A study by Nickell and colleagues elaborated on this impact and contributed towards the knowledge by carrying out the study in a Canada based teaching hospital during 2003 when the outbreak was at the peak. Emotional distress, psychiatric comorbidity, huge concerns for personal and family health were reported by the participants (Nickell et al., 2004). The substantial rise in anxiety is associated with deaths, news and quarantine (Lima et al., 2020).
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".