Managing Missing Data in the Hospital Survey on Patient Safety Culture: A Simulation Study
Bibliographic record
Abstract
BACKGROUND: Case-wise analysis is advocated for the Hospital Survey on Patient Safety culture (HSOPS). OBJECTIVES: Through a computer-intensive simulation study, we aimed to evaluate the accuracy of various imputation methods in managing missing data in the HSOPS. METHODS: Using the original data from a cross-sectional survey of 5064 employees at a single university hospital in France, we produced simulation data on two levels. First, we resampled 1000 completed data based on the original 3045 complete responses using a bootstrap procedure. Second, missing values were simulated in these 1000 completed case data for comparison purposes, using eight different missing data scenarios. Third, missing values were imputed using five different imputation methods (1, random imputation; 2, item mean; 3, individual mean; 4, multiple imputation, and 5, sparse nonnegative matrix factorization. The performance for each imputation method was assessed using the root mean square error and dimension score bias. RESULTS: The five imputation methods yielded close root mean square errors, with an advantage for the multiple imputation. The bias differences were greater regarding the dimension scores, with a clear advantage for multiple imputation. The worst performance was achieved by the mean imputation methods. DISCUSSION AND CONCLUSIONS: We recommend the use of multiple imputation to handle missing data in HSOPS-based surveys, whereas mean imputation methods should be avoided. Overall, these results suggest the possibility of optimizing the HSOPS instrument, which should be reduced without loss of overall information.
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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 | 0.035 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".