iCCareD: The Development of an Algorithm to Identify Factors Associated With Distress Among Caregivers of Children and Youth Referred for Mental Health Services
Bibliographic record
Abstract
Caregiver well-being plays an important role in children's development and a number of factors have been found to impact distress levels among caregivers of children and youth referred for mental health services. Further, caregiver distress impacts youth psychopathology, its acuity as well as related mental health interventions. The purpose of this study was to develop and validate an algorithm for identifying caregivers who are at greatest risk of experiencing caregiver distress. This algorithm was derived from, and will be embedded in, existing comprehensive interRAI child and youth instruments. Ontario data based on the interRAI Child and Youth Mental Health assessment instruments (ChYMH and ChYMH-DD) were analyzed to identify predictors of distress among caregivers of children and youth ages 4-18 years. Starting with proactive aggression, the algorithm uses 40 assessment items to assign one of 30 nodes that are grouped into five levels of risk. The interRAI ChYMH Caregiver Distress (iCCareD) algorithm was validated using longitudinal data from mental health agencies across Ontario and was found to be a good predictor among this sample with a c-statistic of 0.71 for predicting new or ongoing caregiver distress and 65% for both sensitivity and specificity using algorithm values of 3 or greater. This algorithm provides an evidence-based decision-support tool embedded within a comprehensive assessment tool that may be used by clinicians to inform their selection of supports and services for families.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".