Experiences and Lessons Learned from Implementing the RELIEF Digital Symptom Self-Reporting App in a Palliative Home Care Setting
Bibliographic record
Abstract
The majority of Canadians agree they have the right to end-of-life care in their own homes. While a palliative approach to care in the home setting has been demonstrated to be beneficial for patients and the healthcare system, it has rarely been well-integrated through an eHealth approach. Thus, in 2018, we piloted the RELIEF app, a digital symptom self-reporting tool for patients with palliative care needs. This was followed by the initiation of an extension phase of RELIEF in the home care setting. In this commentary, we share the implementation perspectives and experiences of the researchers and healthcare workers involved in this home care phase. It was mainly expressed that there were challenges with nurses feeling involved, supporting the research program, and using the technology, while patients and family caregivers had challenges using the app and cooperating with staff. We describe our lessons learned from these experiences and future changes to be enacted. A detailed report of this trial will be made available in future publications.
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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 | 0.026 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".