Ten tips for improving your clinical practice during the COVID-19 pandemic
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review provides ten tips for improving clinical practice during COVID-19 as pandemic fatigue begins to complicate personal and professional lives of clinicians. RECENT FINDINGS: COVID-19 has created unique and unexpected challenges to healthcare delivery, but has also provided opportunities for re-evaluation of practice patterns to optimize high-value practices. With ongoing uncertainty, key factors to appreciate for patient and population health include the continued touchstones of empathy and compassion, the use of effective risk communication with shared clinical decision-making when appropriate, attention to resource stewardship and vulnerable populations, importance of health literacy and need for critical assessment of media and medical literature to mitigate misinformation, and the hidden costs of the pandemic on children. Although there has been some international concern for allergic reactions to the recently approved Pfizer-BioNTech COVID-19 vaccine, neither the United States Pfizer-BioNTech or Moderna COVID-19 vaccine emergency use authorizations exclude patients without a specific allergy to a vaccine component from receiving vaccination. SUMMARY: Practical adjustments to practice during COVID-19 are feasible and acceptable. Experience during COVID-19 reinforces the critical need for human connection while providing care and service in every encounter.
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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".