Physiotherapists’ role during hospital-to-home transition for older adults with hip fracture and mobility limitation: A research protocol
Bibliographic record
Abstract
Introduction Functional deficits such as gait speed, muscle strength or reduced activities in daily living after discharge are predictors for hospital readmission for older adults with hip fractures. However, physiotherapists (PTs) who are inherently mobility experts, do not actively participate during the hospital-to-home transition of older adults with hip fractures in the developing countries, including Nigeria. This qualitative study aims to describe and explore how PTs working within inpatient rehabilitation units prepare older adults (≥60 years) with a hip fracture for transfer to their home in the community. Methods We will adopt Sally Thorne’s Interpretive Description approach to purposively select 25 PTs with 5-years experience of participating in discharging older adults with hip fractures from inpatient rehabilitation-to-home. Data collection will include (a) semi-structured, one-on-one interviews with PTs, (b) discharge summaries of two older adults, and (c) final focus group discussion with PTs. We will ask the physiotherapists to provide discharge summaries of two older adults - one that they described as a “difficult” case and one that they described as an “easy” case during inpatient rehabilitation-to-home transition. Data will be analyzed employing Sally Thorne’s “borrowing techniques”- content and thematic analysis for the patients’ discharge summaries and PT interviews, respectively.
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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 | 0.057 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.035 | 0.008 |
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".