Making Hospital Care Safer and Better: The Structure-Process Connection Leading to Adverse Events
Bibliographic record
Abstract
The 2004 Canadian Adverse Events (AE) Study which focused on estimating the incidence of AEs in hospital settings found an annual AE rate of 7.5% of all hospital admissions in Canada (Baker et al. 2004). Although study findings in terms of patient outcomes are essential for hospitals to review their own practices, a thorough understanding of the contributing factors leading to adverse events will be an important next step for hospitals to correct the deficiencies that might act as barriers to providing safe patient care. In other words, a thorough investigation and analysis of structure and process factors within individual hospitals as they relate to patient outcomes is needed as a next step. In order to explore how structural and care-process factors might be linked to the occurrence of adverse events in hospitals, this article aims at building a predictive model, by using Donabedian's model of structure, process, outcome and analyzing secondary data of Ontario Registered Nurse Survey of Hospital Characteristics. Results revealed that perceived understaffing, inadequate support services, unpleasant work environment, poor teamwork and non-supportive administration impact negatively the number of tasks left undone by nurses, which in turn, influence negatively the occurrence and frequency of adverse events in hospitals. Results and implications are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".