A positive risk approach when clients choose to live at risk: a palliative case discussion
Bibliographic record
Abstract
PURPOSE OF REVIEW: The article discusses recent approaches in the literature about clients who chose to live at risk in their homes. It argues for a positive risk-based approach and a tool to help manage risk in the home, and applies these to a hypothetical end-of-life scenario. RECENT FINDINGS: Historically, safety plans to consider risk management involved a culture of risk aversion supported by sometimes paternalistic motives intended to protect vulnerable clients. New findings in the literature engage in a process that respects the ethical principles underlying harm reduction philosophies. The literature also argues for a perspective that moves away from viewing risk as only harmful, to one that supports a positive understanding of risk as part of a client's informed choice. SUMMARY: A risk support management plan, based on a positive approach, can provide a way to both support a client's choice to live at risk, anticipate for expected complications, and inform the creation of a contingency plan to address concerns as they may arise. The added value of a structured approach like the one proposed here for risk support management plans is that it provides adequate due diligence and informed decision-making when planning for risk-taking in complex situations.
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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 | 0.002 | 0.009 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".