Protocol for a scoping review of patient–clinician digital health interventions for the population with hip fracture
Bibliographic record
Abstract
INTRODUCTION: Patient-clinician digital health interventions can potentially improve the care of patients with hip fracture transitioning from hospital to rehabilitation to home. Assisting older patients with a hip fracture and their caregivers in managing their postsurgery care is crucial for ensuring the best rehabilitation outcomes. With the increased availability and wide uptake of mobile devices, the use of digital health to better assist patients in their care has become more common. Among the older adult population, hip fractures are a common occurrence and integrated postsurgery care is key for optimal recovery. The overall aims are to examine the available literature on the impact of hip fracture-specific patient-clinician digital health interventions on patient outcomes and healthcare delivery processes; to identify the barriers and enablers to the uptake and implementation of these digital health interventions; and to provide strategies for improved use of digital health technologies. METHODS AND ANALYSIS: We will conduct a scoping review using Arksey and O'Malley's methodology framework and following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Statement for the Scoping Reviews reporting format. A search strategy will be developed, and key databases will be searched until approximately May 2022. A two-step screening process and data extraction of included studies will be performed by two reviewers. Any disagreement will be resolved by consensus or by a third reviewer. For the included studies, a narrative data synthesis will be conducted. Barriers and enablers identified will be mapped to the domains of the Theoretical Domains Framework and related strategies will be provided to guide the uptake of future patient-clinician digital health interventions. ETHICS AND DISSEMINATION: This review does not require ethics approval. The results will be presented at a scientific conference and published in a peer-reviewed journal. We will also involve relevant stakeholders to determine appropriate approaches for dissemination.
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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 | 0.137 | 0.172 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.014 | 0.018 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.138 | 0.029 |
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".