Designing a Mission statement Mobile app for palliative care: an innovation project utilizing design-thinking methodology
Bibliographic record
Abstract
BACKGROUND: Eliciting individual values and preferences of patients is essential to delivering high quality palliative care and ensuring patient-centered advance care planning. Despite advance care planning conserving healthcare costs by up to 36%, reducing psychological distress of patients and caregivers, and ensuring palliative care delivery in line with patient wishes, less than 33% of adults engage in it. We aimed to develop a mobile application intervention to address the challenges related to advance care planning and improve the delivery of palliative care. METHODS: Design-thinking methodology was used to develop a mobile application, in response to issues prominently identified in current palliative care literature. RESULTS: Issues surrounding communication of patient values from both the patient and provider side is identified as a main issue in palliative care. We designed a mobile application intervention prototype to address this. CONCLUSIONS: Our "Mission Statement" mobile application will allow patients to create a mission statement identifying what they want their care team to know about them, as well as space to identify important values and preferences. Patients will be able to evolve their mission statement and values and preferences over the course of their palliative care journey through the application. Design-thinking methodology is an effective tool to drive healthcare innovation and bridge the gap between research findings and implementation.
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 | 0.023 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".