Community pharmacist-and psychologist-led program of neuropsychological screening in the aftermath of the COVID-19 pandemic: A cross-sectional survey
Bibliographic record
Abstract
Background: Early detection and diagnosis of cognitive impairment are paramount to improving the clinical outcomes and care of patients. Whilst primary care professionals play a key role in the healthcare management and treatment of their patients, community pharmacists and other allied health professionals working in community pharmacies are more accessible and trusted. As such, they are in an ideal position to identify and assist in the management of individuals with cognitive memory disorders. Aim: To assess the impact of a pharmacist-based cognitive memory screening service delivered in community pharmacy practice in Italy in the aftermath of the COVID-19 pandemic. Design: Cross-sectional questionnaire-based survey. Setting: Community pharmacies. Population: Patients accessing community pharmacies. Methods: Participants underwent a comprehensive neuropsychological screening program (the “Montreal Cognitive Assessment” (MoCA) test, the “Babcock Story Recall Test”, and the “Rey–Osterrieth complex figure” (ROCF) test). The accuracy, sensitivity, and specificity of the classical medical/psychological referral for cognitive impairment were computed. Results: A sample of 185 subjects (aged 61.24±15.06 years, 78.9% females) was recruited. The classical medical/psychological referral yielded an accuracy ranging from 58.4% to 63.2%, a sensitivity of 56.3-66.7%, and a specificity of 57.9-74.0% in terms of detection of individuals with cognitive impairment. The neuropsychological screening enabled the identification of a further 33.3-43.8% of subjects that would have been missed otherwise. Conclusions: Neuropsychological screening programs in the setting of community pharmacies are highly valuable and effective.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".