Words of wisdom – patient perspectives to guide recovery for older adults after hip fracture: a qualitative study
Bibliographic record
Abstract
Claire Schiller,1,2,* Thea Franke,1,* Jessica Belle,2 Joanie Sims-Gould,1,2 Joanna Sale,3,4 Maureen C Ashe1,2 1Centre for Hip Health and Mobility, Robert H N Ho Research Centre, Vancouver, BC, Canada; 2Department of Family Practice, University of British Columbia, Vancouver, BC, Canada; 3Musculoskeletal Health and Outcomes Research, Li Ka Shing Knowledge Institute, St Michael’s Hospital, Toronto, ON, Canada; 4Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada *These authors contributed equally to this work Abstract: Recovery after hip fracture is complex involving many transitions along the care continuum. The recovery process, and these transitions, often present significant challenges for older adults and their families and caregivers. There is an identified need for more targeted information to support older adults and their families throughout the recovery process.Therefore, our goal was to understand the recovery phase after hip fracture from the patient perspective, and identify specific messages that could be integrated into future educational material for clinical practice to support patients during recovery. Using a qualitative description design guided by a strengths-based focus, we invited men and women 60+ years with previous hip fracture and their family members/caregivers to participate in interviews. We used purposive criterion sampling within the community setting to recruit participants. We followed a semi-structured guide to conduct the interviews, either in person or over the telephone, and focused questions on experiences with hip fracture and factors that enabled recovery. Two investigators coded and analyzed interview transcripts to identify key messages. We interviewed a total of 19 participants: eleven older adults who sustained a hip fracture and eight family member/caregivers. Participants described three main messages that enabled recovery: 1) seek support; 2) move more; and 3) preserve perspective. Participants provided vital information about their recovery experience from hip fracture. In future, this knowledge can be incorporated into patient-centered education and shared with older adults, their families, and health care professionals across the continuum of care. Keywords: hip fractures, qualitative research, patient-centered care, education
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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 | 0.024 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".