BRIGHT IDEAS PROBLEM-SOLVING SKILLS TRAINING FOR CAREGIVERS OF CHILDREN WITH SICKLE CELL DISEASE: A TWO-SITE PILOT FEASIBILITY TRIAL
Bibliographic record
Abstract
Bright IDEAS Problem-Solving Skills Training (BI) is an evidence-based behavioral intervention that has been utilized extensively with caregivers of children recently diagnosed with cancer. Considerable evidence has shown that BI is acceptable to caregivers and improvements in problem-solving skills mediate reduced symptoms of distress; and it is most effective with single, minority caregivers. A slightly modified version of BI was offered to caregivers of children with sickle cell disease (SCD) in a two-site pilot feasibility trial. BI was modified to reduce barriers to care, logistical challenges, and stigma associated with receiving behavioral health services. Our goal was to establish high rates of recruitment and retention amongst caregivers of children with SCD. Recruitment was acceptable (94%; N = 72) and retention reasonable (48.6%) across both sites with 35 caregivers successfully completing the BI program. Results showed that caregivers of children with SCD, who successfully completed the BI program reported, significant improvements in problem-solving skills immediately and three months post-intervention completion. Interestingly, initial levels of distress were low with few caregivers reporting clinically significant levels of distress; distress remained low over time. Findings are discussed in the context of psychosocial screening and assumptions regarding caregivers of children with SCD.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".