Modeling the hospital safety partnership preferences of patients and their families: a discrete choice conjoint experiment
Bibliographic record
Abstract
BACKGROUND: Patients and their families play an important role in efforts to improve health service safety. OBJECTIVE: The objective of this study is to understand the safety partnership preferences of patients and their families. METHOD: We used a discrete choice conjoint experiment to model the safety partnership preferences of 1,084 patients or those such as parents acting on their behalf. Participants made choices between hypothetical safety partnerships composed by experimentally varying 15 four-level partnership design attributes. RESULTS: Participants preferred an approach to safety based on partnerships between patients and staff rather than a model delegating responsibility for safety to hospital staff. They valued the opportunity to participate in point of service safety partnerships, such as identity and medication double checks, that might afford an immediate risk reduction. Latent class analysis yielded two segments. Actively engaged participants (73.3%) comprised outpatients with higher education, who anticipated more benefits to safety partnerships, were more confident in their ability to contribute, and were more intent on participating. They were more likely to prefer a personal engagement strategy, valued scientific evidence, preferred a more active approach to safety education, and advocated disclosure of errors. The passively engaged segment (26.7%) anticipated fewer benefits, were less confident in their ability to contribute, and were less intent on participating. They were more likely to prefer an engagement strategy based on signage. They preferred that staff explain why they thought patients should help make care safer and decide whether errors were disclosed. Inpatients, those with immigrant backgrounds, and those with less education were more likely to be in this segment. CONCLUSION: Health services need to communicate information regarding risks, ask about partnership preferences, create opportunities respecting individual differences, and ensure a positive response when patients raise safety concerns.
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 | 0.000 | 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 | 0.000 | 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".