EXPLORING THE IMPLEMENTATION OF THE STRENGTHENING A PALLIATIVE APPROACH IN LONG-TERM CARE PROGRAM IN A CLINICAL TRIAL
Bibliographic record
Abstract
Abstract Despite the high mortality rates in long-term care (LTC) homes, most do not have a formalized palliative program. Hence, our research team developed the Strengthening a Palliative Approach in Long Term Care (SPA-LTC) program. This presentation will address how the SPA-LTC intervention was designed to be scaled up in a large randomized control trial, related implementation issues and strategies used to address them during the trial. This study used a cross-jurisdictional, effectiveness-implementation type II hybrid cluster design to evaluate the SPA-LTC program in 18 LTC homes across three provinces in Canada. The implementation component was designed as a formative evaluation to explore how to adapt the SPA-LTC program in real time. The SPA-LTC intervention leveraged both internal (palliative champion team) and external supports (palliative consultants) to build capacity within the LTC home to support and prepare residents and families for end-of-life decision making. Due to staff shortages and their inability to attend lengthy education sessions, we held four one-hour education sessions for the palliative champion team that were led by external palliative consultants over three months. Reach of the intervention was compromised as approximately 10% of participating residents died before a palliative care conference could be held. As a result, we prioritized residents who had lower Palliative Performance Scale (PPS) scores when planning a care conference. Clearly, efforts are needed to evaluate both the implementation and effectiveness of complex interventions, such as a palliative program, within a pragmatic approach given the real-life challenges that currently exist in this sector.
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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 | 0.100 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".