Moving towards an enhanced community palliative support service (EnComPaSS): protocol for a mixed method study
Bibliographic record
Abstract
BACKGROUND: The challenge of an ageing population and consequential increase of long term conditions means that the number of people requiring palliative care services is set to increase. One UK hospice is introducing new information and communication technologies to support the redesign of their community services; improve experiences of existing patients; and allow efficient and effective provision of their service to more people. Community Palliative Care Nurses employed by the hospice will be equipped with a mobile platform to improve communication, enable accurate and efficient collection of clinical data at the bedside, and provide access to clinical records at the point of care through an online digital nursing dashboard. It is believed that this will ensure safer clinical interventions, enable delegated specialist care deployment, support the clinical audit of patient care and improve patient safety and patient/carer experience. Despite current attempts to evaluate the implementation of such technology into end of life care pathways, there is still limited evidence supporting the notion that this can be sustained within services and implemented to scale. This study presents an opportunity to carry out a longitudinal evaluation of the implementation of innovative technology to provide evidence for designing more efficient and effective community palliative care services. METHODS: A mixed methods approach will be used to understand a wide range of organisational, economic, and patient-level factors. The first stage of the project will involve the development of an organisational model incorporating proposed changes resulting from the introduction of new novel mobile technologies. This model will guide stage two, which will consist of gathering and analysing primary evidence. Data will be collected using interviews, focus groups, observation, routinely collected data and documents. DISCUSSION: The implementation of this new approach to community-based palliative care delivery will require significant changes to established working patterns. This new service delivery model is being developed by the Hospice in collaboration with a team of international academic, industry, and clinical commissioning service improvement specialists. The findings from this initial evaluation will provide valuable baseline evidence regarding the delivery of palliative and end-of-life care services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".