Case Management on the Front Lines of COVID-19
Bibliographic record
Abstract
PURPOSE: Since the outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the disease known as COVID-19, case management has emerged as a critical intervention in the treatment of cases, particularly for patients with severe symptoms and medical complications. In addition, case managers have been on the front lines of the response across the health care spectrum to reduce risks of contagion, including among health care workers. The purpose of this article is to discuss the case management response, highlighting the importance of individual care plans to provide access to the right care and treatment at the right time to address both the consequences of the disease and patient comorbidities. PRIMARY PRACTICE SETTINGS: The COVID-19 response spans the full continuum of health and human services, including acute care, subacute care, workers' compensation (especially catastrophic case management), home health, primary care, and community-based care. IMPLICATIONS FOR CASE MANAGEMENT PRACTICE: From the earliest days of the pandemic, case managers have assumed an important role on the front lines of the medical response to COVID-19, ensuring that procedures are in place for managing a range of patients: those who were symptomatic but able to self-isolate and care for themselves at home; those who had serious symptoms and needed to be hospitalized; and those who were asymptomatic and needed to be educated about the importance of self-isolating. Across the care spectrum, individualized responses to the clinical and psychosocial needs of patients with COVID-19 in acute care, subacute care, home health, and other outpatient settings have been guided by the well-established case management process of screening, assessing, planning, implementing, following up, transitioning, and evaluating. In addition, professional case managers are guided by values such as advocacy, ensuring access to the right care and treatment at the right time; autonomy, respecting the right to self-determination; and justice, promoting fairness and equity in access to resources and treatment. The value of justice also addresses the sobering reality that people from racial and ethnic minority groups are at an increased risk of getting sick and dying from COVID-19. Going forward, case management will continue to play a major role in supporting patients with COVID-19, in both inpatient and outpatient settings, with telephonic follow-up and greater use of telehealth.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".