Patient safety on weekends and weekdays: A comparative study of two hospitals in California
Bibliographic record
Abstract
Background: Most clinicians believe that hospitals are less safe on the weekends, but the research findings have been mixed. In addition, the investigations have largely examined the outcomes of patients admitted on weekends versus weekdays and not patient harm that occurred on weekends against patient harm that occurred during the week. Objective: To compare the extent of patient harm that occurred on weekend days with the harm that occurred on weekdays. Methods: Using daily incident report data for an entire year from two hospitals in California we measured the number of incidents each day, the average harm per incident, and the total daily harm from all incidents. Analyses were done separately for the two different hospitals and controlled for daily patient census. Harm per incident was assessed to determine whether reporting patterns on weekdays differed from weekends. Results: There were fewer incidents per day and less total daily harm on weekend days than days during the workweek in both hospitals (p < .05). Patient to nurse ratios are held at the same level across all days and shifts. There did not appear to be a systematic tendency to under-report incidents on the weekends. Conclusion: The data strongly suggest that there is less harm to patients due to healthcare error on the weekends than during the week. Further work is needed to determine whether these findings would apply in hospitals with varying staffing levels.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".