Measuring functioning among youth using the Columbia impairment scale: investigating dimensionality and measurement invariance among 14–17 year olds using mental health services and their caregivers
Bibliographic record
Abstract
BACKGROUND: Despite being a widely used and recommended measure of functioning, the Columbia Impairment Scale (CIS) lacks consensus on scale structure and whether child- and parent-report versions measure the same construct(s). This study aimed to better understand the structure and test for measurement invariance across groups of youth and their caregivers. METHODS: The sample included youth 14-17 years of age accessing mental health services, and their caregiver (most often mother), recruited from one of five mental health outpatient hospital sites in Toronto, Canada between September 2016 and March 2020. Exploratory Structural Equation Modeling (ESEM) was used to investigate dimensionality and test for measurement invariance using standard model fit statistics. RESULTS: A total of 189 youth-caregiver dyads were included in the analysis. Youth were on average aged 15.7 (sd = 1.1); 64% were female. Caregivers had a mean age of 48.2 (sd = 7.4) and were 87% mothers. Using ESEM, evidence of a three-factor model was found ("work/school", "home/family" and "socializing"), which included several, large conceptually relevant cross-loadings. Using this model, full metric invariance between youth and caregivers was established, but strong evidence of scalar invariance was not found. CONCLUSIONS: While a multi-dimensional model provided the best fit for the CIS, the presence of several large cross-loadings calls into question whether and how the global scale can best be used in clinical and research settings. Lack of evidence of scalar invariance suggests that multi-informant data should be interpreted carefully. Next steps should include testing for essential unidimensionality.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".