An appraisal of the psychometric properties of the Clinician version of the Apathy Evaluation Scale (AES‐C)
Bibliographic record
Abstract
This article examines the psychometric properties of the clinician version of the Apathy Evaluation Scale (AES-C) to determine its ability to characterize, quantify and differentiate apathy. Critical appraisals of the item-reduction processes, effectiveness of the administration, coding and scoring procedures, and the reliability and validity of the scale were carried out. For training, administration and rating of the AES-C, clearer guidelines, including a more standardized list of verbal and non-verbal apathetic cues, are needed. There is evidence of high internal consistency for the scale across studies. In addition, the original study reported good test-retest and inter-rater reliability coefficients. However, there is a lack of replication on these more stable and informative measures of reliability and as such they warrant further investigation. The research evidence confirms that the AES-C shows good discriminant, convergent and criterion validity. However, evidence of its predictive validity is limited. As this aspect of validity refers to the scale's ability to predict future outcomes, which is important for treatment and rehabilitation planning, further assessment of the predictive validity of the AES-C is needed. In conclusion, the AES-C is a reliable and valid measure for the characterization and quantification of apathy.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".