The next three epochs: Health system challenges amidst and beyond the COVID‐19 era
Bibliographic record
Abstract
The COVID-19 pandemic has brought to light tremendous gaps and issues faced by health systems globally. Commendable effort has been made to retain continuity of care for non-COVID-19 patients amidst the pandemic, particularly using technology-enhanced models of care. However, these efforts are not sufficient to tackle the impending challenges that health systems around the world will face next: (1) vaccine uptake and hesitancy; (2) a mental health crisis; and (3) post-COVID-19 migration. In this letter to the editor, explanation of why each of these issues is concerning and how each subsequent issue grows in severity is provided. Particular focus on the issue of post-COVID-19 migration is made, as this challenge is quite pressing to health systems but has yet to be explored thoroughly in the literature. Possible strategies for health system planners to consider are provided in this letter. Strategies include involving stakeholders such as patients and clinicians in deliberations and deployment of interventions, focussing efforts on adapting primary health systems, and building on technology-enhanced models of care where possible. By adhering to the recommendations made in this letter, health systems may be able to proactively deal with the identified challenges before they become crises of their own, post COVID-19.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.033 | 0.033 |
| 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".