When clients and practitioners have differing views of risk: Benchmarks for improving assessment and practice
Bibliographic record
Abstract
Abstract The assessment of risk is a top priority within routine counselling and psychotherapy services. However, staff often receive little training in this area. Research suggests that differences between practitioner‐rated and client self‐report assessments are to be expected and has indicated that the rates of difference can be relatively high (i.e., >50%). However, no national benchmarks have yet been presented which allow both practitioners and services to assess their degree of difference between client‐ and therapist‐ratings of risk. This study uses data drawn from the CORE National Research Database and the risk domain of the CORE‐OM (n=25338) to address this issue. Percentage of difference in assessment rates are presented to enable services to compare their rates of difference with those obtained in other services. The CORE‐OM risk domain identified 44% of clients as ‘at risk’ while the practitioner assessment identified 10% of clients as being ‘at risk’. For the overall sample, 18% of clients were classified by the practitioner as presenting no risk when the CORE‐OM risk domain identified them at risk. There were large variations between services. The practical use and implications of the results presented are discussed by managers of NHS primary care counselling 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 | 0.273 | 0.465 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".