Bibliographic record
Abstract
COVID-19 has drastically impacted healthcare delivery across the United States and globally. This article outlines the strategic challenges of a free clinic in Milwaukee, Wisconsin, during the pandemic and describes various responses to these challenges. Communication with patients and staff, loss of volunteer practitioners and employee relations are spe- cifically explored. The author argues that implicit aspects of the free-clinic business model positively impacted clinic re- silience and suggests that lessons in workplace culture could be applied across sectors, with the aim of improved resilience during difficult times in the future. RÉSUMÉ Le COVID-19 a eu un effet considérable sur la disponibilité des soins de santé aux États-Unis et dans le monde. Cet article décrit les défis stratégiques confrontant une clinique gratuite à Milwaukee, Wisconsin, pendant la pandémie et recense di- verses réponses à ces défis. L’article explore en particulier la communication avec les patients et le personnel, la perte de praticiens bénévoles et les relations avec les employés. L’auteur soutient que certains aspects implicites du modèle d’en- treprise que représente la clinique gratuite ont eu un impact positif sur la résilience des cliniques en général et suggère que certaines leçons provenant de la culture d’entreprise de celles-ci pourraient s’appliquer à des secteurs différents dans le but d’améliorer leur propre résilience lors de futures périodes difficiles.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.131 | 0.046 |
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".