Adaptation and Feasibility of the interRAI Family Carer Needs Assessment in a Pediatric Setting
Bibliographic record
Abstract
Family carers of children with serious illness contribute many hours of medical care in addition to usual daily care. Assessing the needs and supports of family carers is not routine practice. This study is the first to utilize the interRAI Family Carer Needs Assessment in carers of children, seeking to evaluate and improve its ability to capture their needs. This is a prospective pilot study of family carers of children with serious illness receiving care at a pediatric hospice. Thirty carers completed the self-assessment form. Additional feedback was sought inquiring about the appropriateness of questions and missing information relevant to the pediatric setting. All participants reported the assessment captured important information across multiple domains. Additional questions surrounding extra costs, home and school supports, as well as direct impacts of caregiving activities on pain and relationships were identified as important adaptations. The most common unmet needs in carers and care recipients were episodic relief from caregiving (n=17) and housing adaptation (n=17), respectively. Overall, a comprehensive assessment form is feasible in identifying the diverse needs of family carers of children. Future research should focus on using pediatric specific interRAI tools to guide improvements in policy and practice that can address unmet needs.
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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 | 0.023 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".