An Environmental Scan of Caregiver Support Resources Provided by Hospice Organizations
Bibliographic record
Abstract
BACKGROUND: Informal caregiver support programs offered by hospice organizations support the health and wellbeing of clients and caregivers. However, an understanding of the best practices for informal caregiver support programs currently undertaken across Canada remains unknown, particularly across the province of British Columbia. AIM: The aim of the present study was to describe what existing resources and supports are provided by hospice organizations for informal caregivers of persons who are nearing end of life or who are recently bereaved in British Columbia, Canada. METHODS: In this descriptive study, two thirds of hospice organizations (N = 42/66; 26 urban, 16 rural) participated in a semi-structured telephone interview focused on informal caregiver support programs. All interviews were recorded, transcribed and analyzed thematically and descriptive statistics were employed. FINDINGS: While no one-size-fit-all caregiver support program emerged as a gold standard across all hospice organizations, nearly two thirds (n = 26/42) offered one or more informal caregiver support programs. Four categories of caregiver support programs emerged from the data analysis, including companioning, bereavement and grief supports, education and service supports, and respite for caregivers. CONCLUSION: Caregiver support programs are a valuable service provided by some but not all hospice organizations across British Columbia, Canada. Future studies are needed to determine best methods for hospice organizations to formally assess caregivers' needs and to determine the success and effectiveness of such programs in support of program expansion and evaluation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".