Hidden in plain sight: Addressing the unique needs of high‐risk psychiatric populations during the <scp>COVID</scp>‐19 pandemic
Notice bibliographique
Résumé
It is known that epidemics almost never affect populations equally and these inequalities can drive the spread of infections.1 In addition to older adults and residents of long-term care facilities, there are other unrecognized but critically vulnerable groups that require immediate attention in the evolving COVID-19 pandemic.1, 2 This includes populations with severe and persistent mental illness (SPMI) who require uninterrupted access to mental health services for comprehensive treatment with the goal of averting admission. This is a critical goal given the increased susceptibility of patients with SPMI to infections, including the risk of nosocomially acquired COVID-19.3, 4 Because of the current potential for exponential growth in the population incidence and prevalence of COVID-19, there are concerns that health-care systems will become saturated with critically ill patients such that hospital care may need to be rationed amongst those with seemingly less critical illness whose care may be deemed as ‘non-essential.’ This may have significant impact on patients who present to hospital with other severe conditions, including SPMI. Psychiatric care is not ‘non-essential’ during pandemic events like COVID-19; now more than ever, timely psychiatric care is both essential and indispensable.5 It is imperative to design and implement clinically relevant and patient-safety-driven risk-stratification algorithms to guide decision-making for appropriate access to hospital-based psychiatric care. Psychiatric care for patients with mental illness could pragmatically be stratified from ‘essential’ to ‘least essential.’ The ‘essential’ category would capture those with an increased risk of symptom progression and adverse outcomes and/or functional impairment if care is delayed indefinitely, while ‘least-essential’ reflects that access to care is not medically necessary and could safely be modified or postponed for some time. A clear breakdown of COVID-19 cases by at-risk groups would allow for health care to be matched to those in greatest need. Developing policy based on the evolving epidemiology of COVID-19 would be instrumental in guiding the planning and prioritization of health-care resources so that the most vulnerable groups are well served. This remains a crucial need in psychiatry where the most severely ill experience such an incomparable burden of illness. Finally, the effect of the COVID-19 pandemic on essential clinical research will also need to be considered as the crisis profoundly changes patients and treatment systems. Emerging new infectious diseases, such as COVID-19, can exert a significant psychological impact on the psychiatric community with SPMI, which requires flexible and appropriate interventions. It is an area that urgently needs more research. Three elements are required in future research on the psychological impact of such unprecedented biological events on patients with pre-existing SPMI. First, a systemic perspective is warranted. Just as it is important to evaluate the psychosocial impact of emerging infectious diseases on the general population, it is equally important to examine the psychological effects on the oft overlooked, but disproportionately at-risk, population with SPMI. Second, prospective research is essential as the psychological sequelae may persist or evolve over time in unforeseen but injurious ways in such at-risk groups. Longitudinal studies can assess the role of health determinants further with a view towards identifying protective factors and adaptive coping strategies for subsequent application in cases requiring additional intensive interventions. Third, the outcomes of psychosocial interventions in SPMI during pandemic crises should be evaluated. Identifying beneficial therapeutic strategies during pandemic events may facilitate the implementation of more strategic mental health responses for patients with SPMI in order to balance their disproportionate risk while also attempting to prevent the exacerbation of preexisting socioeconomic disparity. Dr Hategan reports book royalties from American Psychiatric Publishing and Springer outside the submitted work. Dr Abdurrahman reports personal fees from Lundbeck (1 February 2019) and personal fees from Janssen (7 January 2019) outside the submitted work.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».