Proceedings of the 4th Annual PROMIS® Health Organization Conference: Global Advances in Methodology and Clinical Science
Bibliographic record
Abstract
O001 ObjectiveCaregiving for children affects caregivers' lives in many significant ways.Stress of caregivers is understudied and under-reported.The objective was to examine how taking care of a child (<18 years old) affects health of caregivers with mostly typically developing children versus children with a medical condition as a first step toward developing a screener to identify caregivers who need additional supports.Methods Caregivers responded to PROMIS-29 and the University of Washington Caregiver Stress Scale (UW-CSS), a self-reported IRT-based item bank.For all scales, the general population score is 50.The samples included a community sample, and caregivers of children with Epileptic Encephalopathies (i.e., severe epilepsy), Muscular Dystrophy (MD) and Down Syndrome (DS).The clinical populations were selected because they represent different types of caregiving stress (e.g., mostly cognitive or physical challenges, or both).The mean scores were compared between the general population and clinical populations using t-tests. ResultsData from a total of 722 caregivers were used; community sample (n=322), DS (n=143), MD (n=129), and epilepsy (n=128).Average age of caregivers was 42 years (SD=9), 83% were female, 82% were white, 73% were married, 17% had high school education or less, 41% were employed full time, and 90% were biological parents.The average child age was 9 years (SD=5).The same pattern emerged across different health domains, including caregiver stress, with the community sample caregivers reporting better health and less stress than any of the clinical samples.The worst caregiver stress was reported by caregivers of children with epilepsy (M=63).Compared to the community sample, PROMIS scores were substantially worse (> 5 points) for anxiety and fatigue (epilepsy, DS, MD), sleep (epilepsy), social (epilepsy) and depression (epilepsy and MD).Conclusions Compared to the community sample, caregivers of children with medical conditions report considerably worse health.
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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 | 0.144 | 0.176 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.034 | 0.013 |
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".