A systematic review of instruments related to family caregivers of palliative care patients
Bibliographic record
Abstract
Support for family caregivers is a core function of palliative care. However, there is a lack of consistency in the way needs are assessed, few longitudinal studies to examine the impact of caregiving, and a dearth of evidence-based interventions. In order to help redress this situation, identification of suitable instruments to examine the caregiving experience and the effectiveness of interventions is required. A systematic literature review was undertaken incorporating representatives of the European Association for Palliative Care's International Palliative Care Family Caregiver Research Collaboration and Family Carer Taskforce. The aim of the review was to identify articles that described the use of instruments administered to family caregivers of palliative care patients (pre and post-bereavement). Fourteen of the 62 instruments targeted satisfaction with service delivery and less than half were developed specifically for the palliative care context. In approximately 25% of articles psychometric data were not reported. Where psychometric results were reported, validity data were reported in less than half (42%) of these cases. While a considerable variety of instruments have been administered to family caregivers, the validity of some of these requires further consideration. We recommend that others be judicious before developing new instruments for this population.
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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 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".