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Enregistrement 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 sur OpenAlexaff
RakeshKumar Chadda

Notice bibliographique

RevueIndian Journal of Social Psychiatry · 2020
Typearticle
Langueen
DomainePsychology
ThématiqueCOVID-19 and Mental Health
Établissements canadiensImpact
Organismes subventionnairesnon disponible
Mots-clésCoronavirus disease 2019 (COVID-19)Pandemic2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Mental illnessMedicineVirologyMental healthPsychiatryInternal medicineDiseaseOutbreakInfectious disease (medical specialty)

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,076
Score d'incertitude au seuil0,367

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,058
Tête enseignante GPT0,375
Écart entre enseignants0,317 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2020
Routes d'admission1
Résumé présentoui

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