Minimally important change on the Columbia Impairment Scale and Strengths and Difficulties Questionnaire in youths seeking mental healthcare
Bibliographic record
Abstract
BACKGROUND: Evidence-based mental health requires patient-relevant outcome data, but many indicators lack clinical meaning and fail to consider youth perceptions. The minimally important change (MIC) indicator designates change as meaningful to patients, yet is rarely reported in youth mental health trials. OBJECTIVE: This study aimed to establish MIC thresholds for two patient-reported outcome measures (PROMs), the Columbia Impairment Scale (CIS) and the Strengths and Difficulties Questionnaire (SDQ), using different estimation methods. METHODS: A sample of 247 youths (14-17 years) completed the CIS and SDQ at baseline and at 6 months in a youth mental health and substance use trial. At 6 months, youths also reported perceived change. Three anchor-based (mean change, receiver operating characteristic analysis, predictive modelling) and three distribution-based methods (0.5 SD, measurement error, smallest detectable change) were compared. FINDINGS: Different methods yielded varying MIC thresholds. Predictive modelling provided the most precise anchor-based MIC: -2.6 points (95% CI -3.6, -1.6) for the CIS and -1.7 points (95% CI -2.2, -1.2) for the SDQ, indicating that score improvements of 12% for the CIS and 8% for the SDQ may be perceived as 'important' by youths. However, correlations between change score and anchor were below 0.5 for both measures, indicating suboptimal anchor credibility. Stronger correlations between the anchor and T2 PROM scores compared with T1 scores suggest the presence of recall bias. All MIC estimates were smaller than the smallest detectable change. CONCLUSIONS: Predictive modelling offers the most precise MIC, but limited anchor credibility suggests careful anchor calibration is necessary. CLINICAL IMPLICATIONS: Clinicians may consider the MIC CI as indicative of meaningful change when discussing treatment impact with patients. TRIAL REGISTRATION NUMBER: NCT02836080.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".