Acceptability, reliability, referential distributions and sensitivity to change in the Young Person's Clinical Outcomes in Routine Evaluation (YP‐CORE) outcome measure: replication and refinement
Bibliographic record
Abstract
BACKGROUND: Many outcome measures for young people exist, but the choices for services are limited when seeking measures that (a) are free to use in both paper and electronic format, and (b) have evidence of good psychometric properties. METHOD: Data on the Young Person's Clinical Outcomes in Routine Evaluation (YP-CORE), completed by young people aged 11-16, are reported for a clinical sample (N = 1269) drawn from seven services and a nonclinical sample (N = 380). Analyses report item omission, reliability, referential distributions and sensitivity to change. RESULTS: The YP-CORE had a very low rate of missing items, with 95.6% of forms at preintervention fully completed. The overall alpha was .80, with the values for all four subsamples (11-13 and 14-16 by gender) exceeding .70. There were significant differences in mean YP-CORE scores by gender and age band, as well as distinct reliable change indices and clinically significant change cut-off points. CONCLUSIONS: These findings suggest that the YP-CORE satisfies standard psychometric requirements for use as a routine outcome measure for young people. Its status as a free to use measure and the availability of an increasing number of translations makes the YP-CORE a candidate outcome measure to be considered for routine services.
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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 | 0.110 | 0.191 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".