Implementing a Novel Interprofessional Caregiver Support Clinic: A Palliative Medicine and Social Work Collaboration
Bibliographic record
Abstract
BACKGROUND: High levels of burden and, in more severe instances, burnout represents a significant issue for caregivers of patients with advanced cancer. Early identification and management of caregiver distress and cultivating caregiver resiliency are seldom considered elements of routine care. AIM: To leverage the complementary expertise of palliative medicine and social work using an integrated model of care to assess and manage caregiver needs. METHODS: This quality improvement initiative involved the design and implementation of a novel and collaborative Caregiver Support Clinic (CSC), providing joint palliative medicine-social work encounters to caregivers of patients with advanced cancer. RESULTS: Caregivers felt the CSC provided a forum to discuss and review relevant, but previously neglected, care elements. The concerted collaborative efforts demonstrated by clinicians were found to be reassuring and comforting. Clinicians felt CSC visits prevented duplicative information gathering processes, enabled the ability to efficiently arrive at recommendations and both ensured continuity with, and avoided fragmentation of, care. CONCLUSIONS: By addressing the needs of caregivers through a dyadic, joint encounter, fragmentation and duplication in care can be reduced and both integrated and coordinated management can be efficiently provided. Caregiver and clinician experiences confirm this model of care for caregivers is likely to be beneficial and feasible.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".