The development and testing of a survey to measure patient and family experiences with injury care
Bibliographic record
Abstract
BACKGROUND: To deliver patient-centered trauma care, we must capture patient and family experiences with the services they receive. We developed and pilot tested a survey to measure patient and family experiences with major injury care. METHODS: We conducted a structured literature review and focus groups to generate survey items. We pilot tested the survey at a Level I trauma center and assessed feasibility of implementation and construct validity with Spearman's correlation coefficients. Open ended questions were qualitatively analyzed to explore whether responses corroborated survey content. RESULTS: We developed a survey with two parts: acute care component (46 items) and post-acute care component (27 items) with nine domains. We offered the survey (acute care component offered before hospital discharge, post-acute care component offered 1-7 months after discharge) to 170 patients/families, of whom 134 (79%) responded. Patients were primarily male (73%) with major injuries (median Injury Severity Score, 18; interquartile range, 16-25). Overall, respondents for both the acute care and post-acute care components of the survey reported being completely (47% vs. 26%), very (37% vs. 38%), or mostly (16% vs. 21%) satisfied with their injury care, whereas a minority reported being slightly (0% vs. 9%) or very (0% vs. 6%) dissatisfied (p = 0.002 Fischer's exact test). Most survey items were significantly correlated with overall satisfaction (46 of 60 items). Almost all qualitatively identified themes matched survey domains, adding support to the survey content. CONCLUSION: This pilot study demonstrates the feasibility of implementing a survey to capture patient and family experiences associated with major injury care and provides preliminary evidence of the instrument's content and construct validity. LEVEL OF EVIDENCE: Epidemiologic study, level III.
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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 | 0.043 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".