Shared care: the barriers encountered by community-based palliative care teams in Ontario, Canada
Bibliographic record
Abstract
To meet the complex needs of patients requiring palliative care and to deliver holistic end-of-life care to patients and their families, an interprofessional team approach is recommended. Expert palliative care teams work to improve the quality of life of patients and families through pain and symptom management, and psychosocial spiritual and bereavement support. By establishing shared care models in the community setting, teams support primary healthcare providers such as family physicians and community nurses who often have little exposure to palliative care in their training. As a result, palliative care teams strive to improve not only the end-of-life experience of patients and families, but also the palliative care capacity of primary healthcare providers. The aim of this qualitative study was to explore the views and experiences of community-based palliative care team members and key-informants about the barriers involved using a shared care model to provide care in the community. A thematic analysis approach was used to analyse interviews with five community-based palliative care teams and six key-informants, which took place between December 2010 and March 2011. Using the 3-I framework, this study explores the impacts of Institution-related barriers (i.e. the healthcare system), Interest-related barriers (i.e. motivations of stakeholders) and Idea-related barriers (i.e. values of stakeholders and information/research), on community-based palliative care teams in Ontario, Canada. On the basis of the perspective of team members and key-informants, it is suggested that palliative care teams experience sociopolitical barriers in an effort to establish shared care in the community setting. It is important to examine the barriers encountered by palliative care teams to address how to better develop and sustain them in the community.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".