Clinician experiences of administering the Essen Climate Evaluation Schema (EssenCES) in a forensic intellectual disability service
Bibliographic record
Abstract
Purpose – Social climate (ward atmosphere) affects numerous treatment outcomes. The most commonly used measure is the Essen Climate Evaluation Schema (EssenCES) (Schalast et al., 2008). Though studies have investigated the psychometric properties of EssenCES in intellectual disability populations, few have focused on the clinical utility, or accessibility of the measure. The purpose of this paper is to examine clinician's experiences of using this measure with this population. Design/methodology/approach – Clinicians experienced in administering EssenCES with forensic intellectual disability patients completed an open-ended questionnaire, which sought qualitative data on their experiences of using EssenCES with this population. Data were analysed using thematic analysis. Findings – A number of issues were raised regarding use of EssenCES with patients with intellectual disability. Four overarching themes arose: Understanding of Language, Commenting on Others, Understanding of Likert Scale, and Scale Positives and Adaptation. Clinicians felt certain items were not uniformly understood by all patients, particularly those that incorporated abstract concepts, double negatives, or complex language. Originality/value – Results suggest forensic intellectual disability patients vary in their ability to understand EssenCES items. This resulted in significant further explanation by the administering clinician, a practice which raised concern regarding reliability. Results provide preliminary evidence to indicate EssenCES use requires further consideration in intellectual disability services, or adaptation for this client group.
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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 | 0.021 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".