Intervention, individual, and contextual determinants to high adherence to structured family-centered rounds: a national multi-site mixed methods study
Bibliographic record
Abstract
Abstract Background Effective communication in transitions between healthcare team members is associated with improved patient safety and experience through a clinically meaningful reduction in serious safety events. Family-centered rounds (FCR) can serve a critical role in interprofessional and patient-family communication. Despite widespread support, FCRs are not utilized consistently in many institutions. Structured FCR approaches may prove beneficial in increasing FCR use but should address organizational challenges. The purpose of this study was to identify intervention, individual, and contextual determinants of high adherence to common elements of structured FCR in pediatric inpatient units during the implementation phase of a large multi-site study implementing a structured FCR approach. Methods We performed an explanatory sequential mixed methods study from September 2019 to October 2020 to evaluate the variation in structured FCR adherence across 21 pediatric inpatient units. We analyzed 24 key informant interviews of supervising physician faculty, physician learners, nurses, site administrators, and project leaders at 3 sites using a qualitative content analysis paradigm to investigate site variation in FCR use. We classified implementation determinants based on the Consolidated Framework for Implementation Research. Results Provisional measurements of adherence demonstrated considerable variation in structured FCR use across sites at a median time of 5 months into the implementation. Consistent findings across all three sites included generally positive clinician beliefs regarding the use of FCR and structured rounding approaches, benefits to learner self-efficacy, and potential efficiency gains derived through greater rounds standardization, as well as persistent challenges with nurse engagement and interaction on rounds and coordination and use of resources for families with limited English proficiency. Conclusions Studies during implementation to identify determinants to high adherence can provide generalizable knowledge regarding implementation determinants that may be difficult to predict prior to implementation, guide adaptation during the implementation, and inform sustainment strategies.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".