Risk: ‘I know it when I see it’: how health and social practitioners defined and evaluated living at risk among community-dwelling older adults
Bibliographic record
Abstract
Older adults are increasingly choosing to stay and age in their home or other place where they normally live, even when a change in their health reduces their ability to live independently creating concerns about their safety. In this context, community practitioners need to be aware of risk assessment and management strategies as they support their clients’ choices when safety is a concern. This requires an understanding of living at risk and an ability to evaluate the client’s risk status. This article is based on a qualitative research study in which we interviewed 12 Canadian community practitioners in 2012 and explored how they defined, perceived, assessed and managed risk and how they balanced their client’s safety and autonomy. We used a grounded theory methodology to collect and analyse the data. We found that participants tended to define living at risk as a judgement about a client’s impairment within an environment that can cause an event that has an increased potential for a negative consequence. We also found practitioners evaluated the client’s risk by considering seven factors: the client’s capacity and their support, the occurrence, imminency and frequency of the event, the severity of the consequences, and the number of other events co-occurring. In this article, we show that practitioners are comprehensive in their evaluation of the client’s risk. Although practitioners saw risk and living at risk from a negative perspective, they were able to acknowledge that it could coexist along a continuum from safe to unsafe.
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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 | 0.028 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".