Designing Clinically Valuable Telehealth Resources: Processes to Develop a Community-Based Palliative Care Prototype
Bibliographic record
Abstract
BACKGROUND: Changing population demography and patterns of disease are increasing demands on the health system. Telehealth is seen as providing a mechanism to support community-based care, thus reducing pressure on hospital services and supporting consumer preferences for care in the home. OBJECTIVE: This study examined the processes involved in developing a prototype telehealth intervention to support palliative care patients involved with a palliative care service living in the community. METHODS: The challenges and considerations in developing the palliative care telehealth prototype were reviewed against the Center for eHealth Research (CeHRes) framework, a telehealth development model. The project activities to develop the prototype were specifically mapped against the model's first four phases: multidisciplinary project management, contextual inquiry, value specification, and design. This project has been developed as part of the Telehealth in the Home: Aged and Palliative Care in South Australia initiative. RESULTS: Significant issues were identified and subsequently addressed during concept and prototype development. The CeHRes approach highlighted the implicit diversity in views and opinions among participants and stakeholders and enabled issues to be considered, resolved, and incorporated during design through continuous engagement. CONCLUSIONS: The CeHRes model provided a mechanism that facilitated "better" solutions in the development of the palliative care prototype by addressing the inherent but potentially unrecognized differences in values and beliefs of participants. This collaboration enabled greater interaction and exchange among participants resulting in a more useful and clinically valuable telehealth prototype.
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 | 0.043 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".