New Normal Context in Health Care Settings after COVID-19 Pandemic: A Narrative Literature Review
Bibliographic record
Abstract
COVID-19 is an infectious disease that rapidly developed into a pandemic status. This deemed a need for new strategies to carry out routine health care activities. The recent practices and adaptations of the system as a response to the pandemic status were called a new normal situation. The aim of the study was to describe principles for adaptation to a new normal context for health care settings in COVID-19 pandemic. This narrative review of literature was conducted based on policy documents, guidelines, and public notices issued by the government and other key policymakers from the United Kingdom, Australia, Singapore, and Canada between June 15, 2020, and July 15, 2020, available on their government websites. The study revealed several principles, namely, enhanced surveillance, phasedown strategy for restoring routine services, vulnerability, dynamics of the service demand, new principles in human resource management, infection control measures, supply and usage of personal protective equipment, demand for intensive-care unit bed capacity, coordination and collaboration internally and externally, promotion and utility of remote care, ensuring equity, pre-hospital communication and assessment before reaching service facility, enhancing clinician participation in local-level decision-making, and risk assessments within all levels of service facility. The results of this study exposed new principles that facilitated managerial decision-making to the adaptation of new strategies. This new normal context created many challenges for resource management, which needed to consider dynamics of demand of services, prevention of spreading infections, and readiness for surge of cases while safeguarding quality and safety.
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 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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".