Identifying and prioritizing recommendations to optimize transitions across the care journey for hip fractures in Canada: A concept mapping study
Bibliographic record
Abstract
Background: Hip fractures are one of the most common fall-related injuries and often lead to functional decline, morbidity, and rehospitalization. After experiencing a hip fracture, patients undergo several transitions in care, both between different healthcare providers and across healthcare sectors. Care transitions can result in poor health outcomes, medication errors, readmissions, and dissatisfaction from patients and families. While a focus has been placed on enhancing care transitions through improving shared decision-making, relationships, communication, information-sharing, and increasing the involvement of family members, there remained a need for more specific detail and priority setting to facilitate actionable strategies for improvement in care transitions for adults with hip fracture. Objective: The aim of this study was to create a list of actionable and prioritized recommendations to improve care transitions for individuals with hip fracture from the perspectives of patients, caregivers, healthcare providers, and decision-makers. Methods: A mixed methods study, using a concept mapping approach was conducted. Key stakeholders (patients, caregivers, healthcare providers, and decision-makers) participated in three steps of concept mapping: brainstorming, sorting and rating and mapping. In the brainstorming sessions, participants generated ideas about what is needed to improve care transitions for hip fracture. The research team synthesized the statements generated into a final list of 74 statements. In the sorting and rating sessions, participants sorted the final statements into thematic piles (clusters) and rated them on a 5-point Likert-type scale based on their importance and priority (1= not at all important/priority; 5= extremely important/extreme priority). In the mapping session, a subset of participants took part in a group discussion to create a visual map, representing the final clusters. Results: Thirty-seven individuals participated in this concept mapping study, with the majority being healthcare providers. Most participants were also female and identified as women. There was limited representation across races, with most participants identifying as white. The final cluster map selected by participants was the 8-cluster map, which contained the following: (1) access to inpatient services and supports across the care continuum; (2) informed and collaborative discharge planning; (3) access to transitional and outpatient services; (4) communication, education and knowledge acquisition; (5) support for care partners; (6) person-centred care; (7) physical, social, and cognitive activities and supports; and (8) provider knowledge, skills, roles and behaviours. Statements were rated relatively high on both importance and priority, with all clusters having a mean rating of greater than three (moderate importance/priority). Participants rated Cluster 8 - provider knowledge, skills, roles and behaviours as the most important (mean=4.32) and highest priority cluster (mean=4.14). Conclusions: For adults with hip fracture, the delivery of integrated care across sectors is important, and of high priority, to patients, caregivers, healthcare providers, and decision-makers. Through the identification of actionable recommendations, future work can focus on the implementing programs and interventions that address the high priority areas.
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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 | 0.023 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".