A health care record review of early mobility activities after fragility hip fracture: Utilizing the French systematic method to inform future interventions
Bibliographic record
Abstract
Abstract Background A fragility hip fracture is a serious injury in older adults. Following a fragility fracture, a large percentage of patients are unable to regain their pre-fracture level of mobility. There are several international guidelines recommending early mobility after surgery. We do not know the utilization of these early mobility recommendations by health care providers within our institution. An evidence to practice gap occurs when there is a failure to implement best practices. Utilization of a systematic method allows for a strategic approach to assessment of an evidence to practice gap. A recent publication of quality standards in Ontario provides an opportunity for a local needs assessment of potential evidence to practice gaps. Objective To identify if there is an evidence to practice gap in health care provider implementation of recommendations for early mobility after fragility hip fracture surgery. Methods A retrospective chart review was performed to document the rates of early mobility activities during the first five days after hip fracture surgery at a large tertiary centre in Toronto, Ontario. Patients with cognitive impairment were included. Results Early mobility activities in this older adult population are initiated in the first five days after surgery to varying degrees. Between 11% - 50% of patients are not participating in early mobility activities, thereby not meeting recommendations. Those with low pre-fracture function and cognitive impairment have lower rates of participation when compared to those with a high pre-fracture function and no cognitive impairment. Conclusions The chart review has identified a paucity of contextual information which may influence health care providers’ behaviours related to early mobility. The chart audit is limited in its ability to assess the systems issues, which may have an influence on the health care provider behaviour. Considering the lack of information in these areas, we have identified that further work is required to explore factors which may be having an impact on the health care provider’s ability to engage the patients in early mobility activities.
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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 | 0.109 | 0.306 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.054 | 0.039 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".