The role of hospital characteristics in patient safety: a protocol for a national cohort study
Bibliographic record
Abstract
BACKGROUND: Substantial expenditures on health care safety programs have been justified by their goal of reducing health care associated-harm (adverse events), but adverse event rates have not changed over the past 4 decades. The objective of this study is to describe hospital-level factors that are relevant to safety in Canadian hospitals and the impact of these factors on hospital adverse events. METHODS: This is a protocol for a national cohort study to describe the association between hospital-level factors and adverse events. We will survey at least 90 (35%) Canadian hospitals to describe 4 safety-relevant domains, chosen based on the literature and expert consultation, namely patient safety culture, safety strategies, staffing, and volume and capacity. We will retrospectively identify hospital adverse events from a national data source. We will evaluate organization-level factors using established scales and a survey, codesigned by the study team and hospital leaders. Hospital leaders, clinical unit leaders and front-line staff will complete the surveys once a year for 3 years, with an anticipated start date of winter 2022. We will use national health administrative data to estimate the rate and type of hospital adverse events corresponding to each 1-year survey period. INTERPRETATION: Analysis of data from this project will describe hospital organizational factors that are relevant to safety and help identify organizational initiatives that improve hospital patient safety. In addition to biyearly reports to the leaders of the participating hospitals, we have a multifaceted and tailored dissemination strategy that includes integrating the knowledge users into the study team to increase the likelihood that our study will lead to improved hospital patient safety.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".