Developing Dignity of Risk Education: Exploratory Needs Assessment With Healthcare Staff
Bibliographic record
Abstract
Abstract The Dignity of Risk (DoR) concept is being applied more to the care of older adults living with frailty, as healthcare systems are being challenged to broaden perspectives and policy to include a human rights lens to care provision. This initiative aims to develop and provide education for health care staff, based on a needs assessment and DoR evidence from the literature. To explore staff perspectives on DoR in the care of older adults, more than 600 surveys were completed at acute care and community sites across Nova Scotia. Main findings include: Most staff have little to no previous knowledge of the DoR, however most felt it moderately-very important to implement into care. Most staff identified the following barriers to implement DoR into practice: a hyperfocus on safety culture, paternalistic attitudes, fear of liability, and ageism. Other challenges included cognitive issues of the older adult and lack of support from manager and team members. Staff identified DoR facilitators as: policies and guidelines reinforcing same, skills in risk management conversations with caregivers, and understanding how decision-making abilities of the older adult can affect DoR application. Education via participatory workshops was developed and provided. Topics included defining DoR and its value in older adult care, ageism, overprotective attitudes, liability, using an informed strength-based risk management approach in care planning, and building confidence in difficult conversations with caregivers. Post workshop surveys were completed and main findings shared in following abstract.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.046 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".