Strategies for the Implementation of an Electronic Fracture Risk Assessment Tool in Long Term Care: A Qualitative Study
Bibliographic record
Abstract
Abstract Background: Older adults in long-term care (LTC) homes experience high rates of fractures, which are detrimental to their quality of life. The purpose of this study is to identify and make recommendations on knowledge translation interventions to implementing an evidence-based Fracture Risk Clinical Assessment Protocol (CAP) in LTC. Methods: Following the Behaviour Change Wheel framework, we conducted focus group interviews with 32 LTC stakeholders (e.g. LTC physicians) to identify barriers and facilitators, suggest intervention options, and discuss whether the identified interventions were feasible. The interviews were transcribed verbatim and analyzed using thematic content analysis. Results: The intervention themes that met the APEASE criteria were minimizing any increase in workload, training on CAP usage, education for residents and families, and persuasion through stories. Other intervention themes identified were culture change, resident-centred care, physical restructuring, software features, modeling in training, education for staff, social rewards, material rewards, public benchmarking, and regulations. Conclusions: To implement the Fracture Risk CAP in LTC, KT interventions centred around minimizing any increase in workload, training on CAP usage, providing education for residents and families, and persuading through stories may be used. Results from this work will improve identification and management of LTC residents at high fracture risk and could inform the implementation of guidelines for other conditions in LTC homes.
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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 | 0.043 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".