Enabling a family caregiver-led assessment of support needs in home-based palliative care: Potential translation into practice
Bibliographic record
Abstract
BACKGROUND: Systematic assessment of family caregivers' support needs and integrating these into service planning according to evidence-based research are vital to improving caregivers' outcomes and their capacity to provide care at end of life. AIM: To describe the experience with and feedback of nurses on implementing a systematic assessment of support needs with family caregivers in home-based palliative care, using the Carer Support Needs Assessment Tool. METHODS: This study was conducted during 2012-2014 in Silver Chain Hospice Care Service in Western Australia. This article reports on one part of a three-part evaluation of a stepped wedge cluster trial. Forty-four nurses who trialled the intervention with 233 family caregivers gave their feedback via surveys with closed- and open-ended questions (70.5% response rate). Analyses of quantitative and qualitative data were undertaken. RESULTS: The feedback of nurses was overwhelmingly positive in terms of perceived benefits in comparison to standard practice both from the family caregiver and service provider perspectives. Using the Carer Support Needs Assessment Tool was described by nurses as providing guidance, focus and structure to facilitate discussion with family caregivers and as identifying needs and service responses that would not otherwise have been undertaken in a timely manner. CONCLUSION: Our study has successfully addressed the call for alternatives to the professional assessment paradigm using the Carer Support Needs Assessment Tool approach as a caregiver-led intervention facilitated by health professionals. Integrating the Carer Support Needs Assessment Tool in existing practice is fundamental to achieving better caregiver outcomes.
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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 | 0.064 | 0.122 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".