The Development and Integration of a Safety Officer Role to Facilitate Prevention of COVID-19 Virus Transmission in an Adult Inpatient Rehabilitation Setting Using Collaborative Change Leadership Methodology
Bibliographic record
Abstract
BACKGROUND: With the onset of the COVID-19 pandemic, a large urban academic hospital responded by creating the temporary role of a "Safety Officer (SO)." The key task of the SO role was to supervise staff donning and doffing personal protective equipment (PPE) and provide real-time feedback on their performance. The support for safe donning and doffing would contribute to staff well-being by reducing their fear of infection transmission. METHODS: A Collaborative Change Leadership (CCL) approach was used to facilitate the development, implementation, and evaluation of the role. This included an iterative feedback process with clinicians and safety officers to continually refine the role. FINDINGS: Feedback indicated value in the initiative as increasing staff confidence about preventing virus transmission, as well as their sense of safety at work. Areas for future improvement included additional communication strategies for interprofessional teams and external partners, as well as planning around logistics to better support the safety officers in performing this new, temporary role. CONCLUSIONS/APPLICATION TO PRACTICE: The Safety Officer role was able to help alleviate concerns regarding potential infection transmission and contribute positively to staff well-being.
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 | 0.038 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".