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Record W3091909241 · doi:10.4103/ijsp.ijsp_239_20

Patients with preexisting mental illness and other vulnerable groups in the COVID-19 pandemic

2020· article· en· W3091909241 on OpenAlexaff
RakeshKumar Chadda

Bibliographic record

VenueIndian Journal of Social Psychiatry · 2020
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsImpact
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Pandemic2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Mental illnessMedicineVirologyMental healthPsychiatryInternal medicineDiseaseOutbreakInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

In the last few months, the whole world has been taken aback by the coronavirus disease 2019 (COVID-19) pandemic, which has probably played more havoc than even the two world wars by affecting most of the countries across almost all continents of the world. Because of its rapid spread across the world, the huge population it has affected, its highly infectious nature, and widespread transmission of the illness despite not a very high fatality rate, COVID-19 has brought a panic-like reaction in the whole world community.[1] The pandemic has been associated with psychosocial reactions and symptoms of stress, anxiety and depression in those detected positive as well as in their contacts and also the health-care workers. Due to the countrywide lockdowns in many countries as well as the diversion of health services from non-COVID to exclusive COVID care facilities, the pandemic has adversely affected the non-COVID health care. This has impacted the already-vulnerable population with preexisting mental illness or severe physical illnesses, whose continuation of care has been affected.[2] The review by Khandelwal in this issue of the journal,[1] very succinctly discusses the psychosocial impact of the pandemic tracing its progress across the world, the initiatives taken up by the Government of India including the countrywide lockdown continuing for >10 weeks, diversion of the general health services to COVID-specific health services, creation of investigation facilities across the country, massive unemployment resulting from the closure of industries and businesses because of the lockdown, and the effects of the reverse migration.[1] The lockdown with most of the population confined to closed doors in their respective houses because of the fear of getting the deadly infection; economic breakdown; and closure of all educational institutions and the entertainment industry as well as various avenues of the outdoor activities including parks, business malls, and markets, along with continuous streaming of news about the increasing number of cases and fatalities related to COVID-19, have all added to the stress reactions in the general population. Stated as the fastest-moving global public health crisis in a century,[1] COVID-19 has brought the health systems across the world including those of the most advanced economies to a halt by its rapid and massive spread. India, fortunately, by clamping a countrywide lockdown, was able to slow down the progress of the infection, though the growth graph of the infection as well as the fatalities has been rising even after 10 weeks of the lockdown, which is now being lifted in a gradual manner.[3] In the face of the pandemic leading to its devastating effects on the economic infrastructure, unemployment, community fear and panic, and psychosocial effects of lockdown and quarantine, persons with preexisting mental health problems and the other vulnerable population need specific attention from the state and the society as also stated in the article.[1] Declaration of the national-level lockdown in India was meant for controlling the spread of infection, but its sudden declaration came out with many adverse consequences. Closure of industrial sector and business establishments led to a large population, working in such places, out of job and economic crisis. This group constituted a large section of population in big cities such as New Delhi, Mumbai, and others. Many of them in the absence of the availability of local transport left their houses and started moving toward their native places, leaving their abodes in the cities where they had lived for decades. Unfortunately, the state and the community were not able to make adequate arrangements or coordinate the needed support for this population in the cities. The period of lockdown though helped the state health sector to organize its services for the COVID-19 pandemic, affected adversely the persons suffering from other illnesses including those with severe mental illnesses on treatment and those with serious physical illnesses such as cancer, cardiac, or illnesses of related nature. Persons with mental illnesses on treatment and those with substance use disorders on substitution treatment and with other illnesses including the life-threatening ones were not able to access the services, leading to multiple adverse consequences. The situation has not even been a healthy one in the advanced economies and countries such as the USA or most from Europe. The National Disaster Management Authority of India, which had been dealing mostly with the natural disasters in India in the recent past, did not expect a biological disaster of this nature which has occurred. Similarly, India needs to increase its health budget, which stands at just about 1% of the gross domestic product, one of the lowest in the world. We need to go for universal coverage for health; an attempt for which has been made in the form of introduction of the Ayushman Bharat Schemes by the Government of India. Another important aspect that needs to be considered is the risk of law and order breaking down in the background of the economic hardships occurring as a result of the COVID-19 pandemic. There was a response from the Union Government of India in the form of declaration of a financial package of Indian rupees 20 lakh crores (equivalent to 265 billion US dollars) along with support services organized by the state governments, the voluntary sector, and the community in general.[4] Many state governments, community organizations, as well as persons in individual capacity organized relief services, but the population affected was so large that a large section of the affected population was not able to get the needed relief. It is important to state here that the relief work needed is of mammoth nature, with bringing back of employment to so many people who lost jobs during the pandemic by resuming the business and industrial sector. Though the government has declared a reasonable financial package, it has to be used in a manner that it benefits those who need it the most. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.367

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.058
GPT teacher head0.375
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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