The PPE spotter role: Supporting best practice in acute and long-term care
Bibliographic record
Abstract
Healthcare workers are at high risk of contracting infections including COVID-19 due to close and frequent contact with patients. To promote appropriate use of personal protective equipment (PPE) and to enhance protection of healthcare workers during the COVID-19 pandemic, we trained a team of registered nurses to serve as “PPE Spotters”. This team offered in-person observation, support, feedback, and on-the-spot teaching about proper PPE use and hand hygiene practices. Evaluation showed staff and leaders felt the Spotters effectively promoted best practices for PPE and hand hygiene, and 86% recommended the program continue. PPE Spotters now serve a formal role in the organization, supporting both acute and long-term care.
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 | 0.066 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".