Identifying and prioritizing recommendations to optimize transitions across the care journey for hip fractures: Results from a mixed-methods concept mapping study
Bibliographic record
Abstract
BACKGROUND: Individuals who experience a hip fracture have numerous care transitions. Improving the transition process is important for ensuring quality care; however, little is known about the priorities of different key interest groups. Our aim was to gather recommendations from these groups regarding care transitions for hip fracture. METHODS: We conducted a concept mapping study, inviting persons with lived experience (PWLE) who had a hip fracture, care partners, healthcare providers, and decision-makers to share their thoughts about 'what is needed to improve care transitions for hip fracture'. Individuals were subsequently asked to sort the generated statements into conceptual piles, and then rate by importance and priority using a five-point scale. Participants decided on the final map, rearranged statements, and assigned a name to each conceptual cluster. RESULTS: A total of 35 participants took part in this concept mapping study, with some individuals participating in multiple steps. Participants included 22 healthcare providers, 7 care partners, 4 decision-makers, and 2 PWLE. The final map selected by participants was an 8-cluster map, with the following cluster labels: (1) access to inpatient services and supports across the care continuum (13 statements); (2) informed and collaborative discharge planning (13 statements); (3) access to transitional and outpatient services (3 statements); (4) communication, education and knowledge acquisition (9 statements); (5) support for care partners (2 statements); (6) person-centred care (13 statements); (7) physical, social, and cognitive activities and supports (13 statements); and (8) provider knowledge, skills, roles and behaviours (8 statements). CONCLUSIONS: Our study findings highlight the importance of person-centred care, with active involvement of PWLE and their care partners throughout the care journey. Many participant statements included specific ideas related to continuity of care, and clinical knowledge and skills. This study provides insights for future interventions and quality improvement initiatives for enhancing transitions in care among hip fracture populations.
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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 | 0.097 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.008 |
| 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".