Strategies for the implementation of an electronic fracture risk assessment tool in long term care: a qualitative study
Bibliographic record
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 strategies to implementing an evidence-based Fracture Risk Clinical Assessment Protocol (CAP) in LTC. METHODS: Following the Behaviour Change Wheel framework, we conducted six focus group interviews with a total of 32 LTC stakeholders (e.g. LTC physicians) to identify barriers and facilitators, suggest implementation strategies, and discuss whether the identified strategies were affordable, practicable, effective, acceptable, safe, and if they promote equity (APEASE). The interviews were transcribed verbatim and analyzed using thematic content analysis. RESULTS: Themes of implementation strategies 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 strategy 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, we recommend using implementation strategies centred around minimizing any increase in workload, training on CAP usage, providing education for residents and families, and persuading through stories. Through improving implementation of the fracture risk CAP, 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".