What does an idiographic measure (PSYCHLOPS) tell us about the spectrum of psychological issues and scores on a nomothetic measure (CORE-OM)?
Bibliographic record
Abstract
Background: Psychological Outcome Profiles ('PSYCHLOPS') is a short, idiographic mental health outcome measure, designed for use in primary care. It has convergent validity with nomothetic measures. Aims: This study determined the proportion of issues elicited by PSYCHLOPS (idiographic) which do not appear in CORE-OM items (nomothetic). The study compared the issues reported on PSYCHLOPS by clients classified as cases and as non-cases on CORE-OM. Methods: The study population consisted of 215 clients referred for talking therapy in primary care. Thematic analysis was conducted to categorise the freetext responses of PSYCHLOPS. These themes were cross-checked against the items in CORE-OM. Pre-therapy CORE-OM scores were used to determine caseness and hence compare PSYCHLOPS themes by caseness. Results: PSYCHLOPS responses were coded into 61 different themes. 'Relationship problems' were the most frequent themes. Twenty-seven of the 61 (44%) themes were not covered by CORE-OM items; 128 (60%) clients reported at least one response which did not clearly map to a CORE-OM item. Sixty-six of the 215 (31%) clients were classified as non-cases and they showed no significant differences in theme frequency from the cases. Conclusions: Idiographic measures highlight the psychological concerns of clients presenting for therapy; many of these concerns did not feature on a nomothetic measure. By using both outcome measures, we have tried to capture diversity and demonstrate the multi-faceted nature of psychological distress rather than to standardise the assessment of outcome
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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 | 0.015 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.003 |
| 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".