Implementation of cognitive assessment and follow‐ups in people complaining of cognitive problems over the age of 50, in community pharmacies by using the Neurocognitive Frailty Index: A pilot study
Bibliographic record
Abstract
BACKGROUND: Pharmacists are an integral and accessible part of the community that play an evolving role in Canadian primary health care (Raiche et al., 2020), while integrating technology in the cognitive assessment process could help with dementia care and management (Astell et al., 2019). The aim of the current ongoing pilot study is to examine how pharmacists' utilization of the digital version of the Neurocognitive Frailty Index (NFI, Pakzad et al., 2017) can improve timely identification of cognitive disorder and contribute to the quality of treatment and care in patients with cognitive complaints, over time. METHOD: Pharmacies across New Brunswick, Canada were invited to participate in the current study as to receive training to offer the NFI assessment tool to their clients aged 50 years and over with repeat assessments spaced 3-12 months apart. Longitudinal analyses will allow for proper follow-up of study participants as to ascertain the pertinence of NFI in tracking cognitive decline, as well as noting the professional healthcare decisions made by pharmacists based on the personalized report generated by the NFI. RESULT: A total of 9 pharmacists have been recruited and trained on the administration of NFI. The first wave of NFI assessments began in November 2024 with 12 individuals tested thus far, as well as receiving a follow-up by their pharmacists regarding scheduling a follow-up NFI evaluation, referral to a primary care provider, having their prescriptions reviewed and/or adjusted, recommending lifestyle changes and/or offering any cognitive pharmaceutical services if available within their respective pharmacies. CONCLUSION: Recruitment and training of additional pharmacists is ongoing, while already active pharmacists have signaled continued interest to offer the NFI to their clients at their respective pharmacies. By the end of this pilot study, the results will help inform whether the implementation and use of the NFI by pharmacists as a companion tool will positively impact the management and outcome of those individuals aged 50 years and over with cognitive impairment.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 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 | 0.000 |
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".