The Utilization of Health Informatics Interventions in the COVID-19 Pandemic: A Scoping Review
Bibliographic record
Abstract
On March 11, 2020, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the highly infectious virus that causes coronavirus disease (COVID-19), was characterized by the World Health Organization (WHO) as a global pandemic [1,2]. Due to its highly contagious nature, COVID-19 has catalyzed the introduction of non-pharmaceutical interventions such as social distancing and quarantine measures [6]. Thus, the pandemic has shifted society to become reliant on healthcare technologies. The objective of this scoping review is to establish what health informatics interventions have been applied, validated and tested globally during the COVID-19 pandemic. The findings demonstrated a range of 12 types of health informatics interventions with various global applications and use. As evidenced by the intervention heterogeneity, the necessity to adopt a global cohesive strategy to improve human safety through the utilization of smart, efficient, and communicable technologies is vital.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".