RESPECT for person-centred palliative and end-of-life care in Ontario’s long-term care homes
Bibliographic record
Abstract
Context: Increasingly, more and more Canadians spend their final days in supportive living environments, such as retirement homes and long-term care (LTC) homes. Despite the average life expectancy of 18 months among those living in LTC, and most can benefit from a palliative approach to care, many only receive end-of-life care in the last 2-3 weeks of life. RESPECT (Risk Evaluation for Support: Predictions for Elder life in their Communities Tool) is a risk communication tool powered by a suite of prediction algorithms that estimate individuals’ survival (that is, how long someone will live) and designed to support earlier identification of palliative care needs. RESPECT was developed and validated using routinely collected population-level data from home and community care and LTC homes in Ontario. Objective: To implement and evaluate the use of RESPECT (i.e., barriers and facilitators) in LTC homes for earlier identification of palliative care needs. Study Design: Qualitative pre-post implementation interviews and focus groups. Population and Setting: Seven focus groups involving 17 registered staff and 19 personal support workers (PSWs). A total of 22 interviews have also been conducted with 13 health organization leaders and 9 frontline staff. Outcome Measures: Data collection and analysis are informed by two implementation science frameworks: the Actor, Action, Context, Target, and Time Framework, and the Theoretical Domains Framework. Analysis: Thematic analysis using established implementation science frameworks. Results: Barriers to implementation include limited time for training and capacity; limited understanding of the tool and how it can be used to inform decisions and facilitate conversations with residents and/or families; lack of clarity on the processes, timelines, and assigned roles for integrating RESPECT into routine procedures; and staff (especially PSWs’) discomfort in knowing residents’ life expectancy. Physicians’ acceptability of RESPECT is an important determinant of successful implementation, given their key role in communicating residents’ prognoses and making medical decisions. Physicians generally feel positive about RESPECT, although many would like more time to observe the tool’s accuracy; nonetheless, they can envision themselves using the tool. Conclusions: Findings from this study suggest a potential role for risk communication tools, like RESPECT, in supporting a palliative approach to care in LTC settings.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".