Bibliographic record
Abstract
As a regional health care leader, safety and high reliability are key elements of service excellence at Bayhealth.As we all continue to discover our new normal, COVID-19 is pushing health care into this new normal as well.The first quarter of 2020 felt like a discovery of unknowns.Unknowns in how we treat COVID-19, how we manage patient care, where we place patients as we run out of beds, what will be allowed as it relates to visitors, how will we manage this crisis from our 24-hour command center, and how will we successfully work with state and federal guidance.Space, testing, supplies and understanding how to treat this new disease dominated those early days.Technology was critical.Technology supported the opening of new care spaces at the Bayhealth Kent and Sussex campuses, as well as temporary locations near our emergency departments.These new spaces were immediately equipped with all the necessary telecommunications, computers, mobile devices, and Wi-Fi capabilities.While we were fortunate to not need many of the additional care spaces created, we were prepared.The need for technology extended beyond direct patient care.Testing and supplies required dashboards and reporting mechanisms to easily send information to the Bayhealth team and state health personnel so our staff could be equipped with the necessary safety supplies to continue caring for our community.
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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.055 | 0.011 |
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".