Computer-Based Patient Bias and Misconduct Training Impact on Reports to Incident Learning System
Bibliographic record
Abstract
OBJECTIVE: To assess the effect of computer-based training (CBT) and leadership communication on incident learning system reports pertaining to institutional policy that targets biased, prejudiced, and racist behaviors of patients and visitors toward health care employees. PATIENTS AND METHODS: Mayo Clinic developed a CBT module and comprehensive communication strategy to educate staff on the Patient and Visitor Conduct Policy. Additional goals were to demonstrate leadership endorsement and support of the policy, teach how to report an incident, and facilitate how policy enforcement might occur. Using descriptive statistics, we compared the reporting data before and after the intervention. RESULTS: Participants were 13,980 employees in 68 clinics and 18 hospitals in the US Midwest. Bias and misconduct incidents entered in the incident reporting system increased 312% (n=140 incidents; preintervention, n=34) in the quarter (ie, 3 months) immediately after intervention. The number of incidents in the next quarter stayed increased (234%; n=114) compared with the preintervention number. Secondary debriefing with employees showed the value of the education and the importance of leadership support at the highest level to facilitate comfort in policy enforcement. CONCLUSION: Institutional policy that targets biased, prejudiced, and racist behaviors of patients toward employees in a health care setting can be augmented with employee education and leadership support to facilitate change. The CBT, paired with a robust communication plan and active leadership endorsement and engagement, resulted in increased reporting of biased, prejudiced, and racist behaviors of patients.
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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 | 0.005 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".