Hidden in plain sight: Addressing the unique needs of high‐risk psychiatric populations during the <scp>COVID</scp>‐19 pandemic
Bibliographic record
Abstract
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.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 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".