The effects of trained observers (dofficers) and audits during a facility-wide COVID-19 outbreak: A mixed-methods quality improvement analysis
Bibliographic record
Abstract
BACKGROUND: In response to a facility-wide COVID-19 outbreak, our tertiary acute care hospital implemented an evidence-based bundle of infection control practices including the use of audits and trained observers "dofficers" to provide real-time constructive feedback. METHODS: We trained furloughed staff to perform the role of dofficer. They offered support and corrective feedback on proper PPE use and completed 21-point audits during a 4-week intervention period. Audits tracked appropriate signage, placement and availability of supplies (equipment), correct PPE use, enhanced environmental cleaning, along with cohorting and social distancing rates. Audit data was used to provide weekly quality improvement reports to units. RESULTS: Nine hundred and sixty two separate audits recorded 36,948 observations, over 7,696 observer-hours. The most common errors were with environmental cleaning and PPE use; the least common were with regards to equipment availability and cohorting and social distancing. Mean error rates decreased from 9.81% to 2.88% (P < .001). The largest reduction, 22.57%, occurred in the category of PPE doffing errors. CONCLUSIONS: Dofficer led audits effectively identified areas for improvement. Feedback through weekly reports and real-time correction of PPE errors by dofficers led to statistically significant improvements; however, error rates remained high. Further research is needed establish if these relationships are causal.
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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 | 0.116 | 0.192 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".