The relative importance of support domains in explaining adverse effects from family care giving
Bibliographic record
Abstract
Background Family carers play a central role in supporting patients at home towards the end of life. However, they often suffer considerable adverse effects from care giving. To improve care provision for carers the authors need to understand the predominant domains of support associated with adverse effects from care giving, where lack of support may have the greatest negative impact. Aims To investigate the relative importance of different support domains in explaining adverse effects from care giving. Methods Six hospice home care services in the UK identified and sent a postal survey to the main carer of all patients active on their caseload. N=225 carers participated (25% response rate). Survey measures included carer Preparedness (Archboldet al), Global Health (EORC QLQ-C30), Strain and Distress (FACQ-PC). Lack of support was measured through 14 core support domains of the Carer Support Needs Assessment Tool (CSNAT). Linear regression analysis was conducted to examine the associations between the support domains and carers' perceived preparedness, global health, strain and distress. The Pratt Index was used to evaluate relative importance. Results Lack of daytime and night-time respite and not knowing what to expect in the future, were the most important CSNAT domains in explaining adverse effects from care giving. Overall lack of support explained the greatest variance in Strain (47%) and Global Health (35%), followed by Distress (29%) and Preparedness (27%). Further details of the relative importance of each support domain on each type of adverse effect will be presented. Conclusions While the authors know general support for carers is likely to be beneficial, our analysis demonstrates the importance of understanding the relative importance of different domains of support in preventing adverse effects, in particular respite and reducing uncertainty about what to expect in the future.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".