Measuring functional impairment: Preliminary psychometric properties of the Columbia Impairment Scale‐Youth Version with youth accessing services at an outpatient substance use programme
Bibliographic record
Abstract
AIM: Functional impairment is a key aspect of mental disorders, yet it is poorly defined and operationalized, particularly for youth. The Columbia Impairment Scale (CIS) has been indicated as a potentially useful measure to assess functional impairment. This study provides an initial psychometric evaluation of the CIS-Youth (CIS-Y) Version in a sample of youth accessing an outpatient substance use programme. METHODS: The CIS-Y Version was administered to youth aged 15 to 24 years accessing an outpatient substance use programme in Ontario, Canada. Demographic data on age, sex, ethnicity and current occupational and/or educational status were also collected. Exploratory factor analysis (EFA) was used to identify what factor structure best fits our sample of youth. RESULTS: The sample included 134 youth, with a mean age of 19.3 (SD = 2.1; range = 15-24). Over 34% of the sample had at least some item-level missing data, overwhelming this was item-level "not applicable" responses. The CIS-Y exhibited good internal consistency (α = .84), and EFA revealed that a one-factor structure was the best fit for the data. CONCLUSIONS: Results suggest that continued use of the CIS-Y with populations of youth, including emerging adults, is warranted. The scale has good internal consistency, loads onto one factor and discriminates between groups known to have lower and higher functioning. Further research is recommended that uses larger and more varying samples, as well as research that investigates optimal coding of non-applicable responses.
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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 | 0.003 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".