Benchmarking key service quality indicators in UK Employee Assistance Programme Counselling: A CORE System data profile
Bibliographic record
Abstract
Abstract Background: Levels of psychological distress appear to be increasing in the workplace, in parallel with the growth of employee assistance programme (EAP) provision offering a range of talking treatments. However, such growth takes place in the absence of a substantive body of supporting research evidence despite a quarter of a decade of research activity. Aims: To analyse a national sample of EAP data and profile relative service quality on a set of key service indicators. Method: CORE System data profiles of over 28,000 clients were voluntarily donated by six EAP service providers. An established benchmarking methodology was used to assess the relative quality of EAP service provision compared with published CORE System benchmarks for NHS primary care and UK higher education student counselling services. Results: High quality data profiled an EAP service clientele who were quantifiably distressed, accessed treatment quickly, with the majority completing treatment and demonstrating high rates of recovery and/or improvement relative to published benchmarks from the NHS and HE comparative sectors. Limitations of the study and implications for practice and further investigation are considered.
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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 | 0.016 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| 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".