Sustaining Palliative Care Teams That Provide Home-Based Care In A Shared Care Model
Bibliographic record
Abstract
This research examined the barriers and facilitators involved in the development and sustainability of palliative care teams using a shared care model. Shared care is established when interdisciplinary specialist palliative care teams (usually comprised of a palliative care physician, an advanced practice nurse, a psychosocial spiritual advisor, a bereavement counselor, a case manager and an administrator) form partnerships with primary care providers (usually frontline family physicians and home care nurses) to support the complex needs of terminally-ill patients and their family members in the home setting. Palliative care teams overcome gaps in the current health care system, such as: lack of palliative care specialists; poor coordination and integration of care, and; a health care workforce with insufficient training in palliative care. This type of service delivery model is common in medical specialties such as mental health and obstetrics, and various forms of palliative shared care have been implemented in other countries such as the US, Australia, UK, Italy and Spain, where it has been shown to be cost-effective. There are few palliative care teams working in a shared care model in Canada; this provided the impetus to investigate the process of how this integrated approach is developed and sustained within the context of specific populations and geographies. A longitudinal case study in a Local Health Integration Network (LHIN) area in Southern Ontario, comprised of urban and rural communities, was conducted in order to evaluate barriers and facilitators in using a shared care model from the perspective of team members, key-informants and stakeholders. The evaluation of barriers and facilitators informed recommendations to guide the sustainability of palliative care teams working in a shared care model.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".