ADOPTING A STRENGTH-BASED, PERSON-CENTERED RISK ASSESSMENT CLINICAL DECISION SUPPORT TOOL: WHAT ARE THE BENEFITS?
Bibliographic record
Abstract
Abstract Most older adults want to age in place even if changes in their health results in home safety concerns. A consistent approach to assessing both the physical and psychological risks associated with the decision to remain at home is lacking. The Living with Risk: Decision Support Approach (LwR:DSA) is a recently validated innovative clinical tool that supports a balanced, systematic and person-centered assessment of risks, by analyzing their negative and positive consequences. The aim of this mixed-method study was to understand the barriers and facilitators to using the LwR:DSA during usual care to determine how best to support widespread adoption. Twenty-two hospital- and community-based clinicians used the LwR:DSA for eight weeks. Individual interviews were performed to document the factors that hindered and helped the use of the LwR:DSA in their clinical setting. The interviews were analyzed using Qualitative Description and the positive impact of using the LwR:DSA emerged as one of the facilitator themes. The participants described that using the LwR:DSA in practice improved their clinical decision making, communication and their ability to provide person-centered care. More specifically, the LwR:DSA 1) helped the clinicians understand the risk level and the context, causes, and consequences of the safety concern; 2) guided them to co-create with the older adult, agreeable recommendations to reduce the risk of adverse outcomes; 3) decreased clinician discomfort and 4) supported authentic conversations with the older adult. Understanding the benefits of using the LwR:DSA provides key information for clinicians by challenging the status quo of their current practice.
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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 | 0.023 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".