Development of a checklist for cognitive assessment requirements (CARE) based on a Delphi consensus study
Bibliographic record
Abstract
Situational factors can influence cognitive performance and should be considered for conducting cognitive assessments. The objective of this project was to develop a checklist for Cognitive Assessment Requirements (CARE) to identify these situational factors before conducting cognitive assessments and account for them. This study employed a four-round Delphi approach involving 22 experts to identify situational factors that can impact cognitive assessment results. The development of a robust and well-balanced checklist was guided by a consensus-driven approach, which considered metrics such as Interquartile Deviation (IQD) (> 1.00), Percentage of Positive Responses (PPR, above 60%), and mean importance ratings (< 3 on a 5-point Likert scale) to assess both degree of agreement and item importance. Consensus was reached, leading to a 14-item checklist to evaluate cognitive assessment requirements. These items were categorized into six groups: Acute Illness or Physical Discomfort, Medication Effects and Substance Use, Sleep Quality and Fatigue, Emotional State, Language factors, and Environmental factors. The CARE can be employed prior to cognitive assessments to identify situational factors of relevance to the individual client, thereby creating a more favorable environment for cognitive evaluation, and enhancing the reliability of the assessment findings. Furthermore, the CARE can help determine the level of confidence in the results by assessing whether the conditions are conducive to testing or if situational factors may undermine the validity of the evaluation.
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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 | 0.221 | 0.238 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".