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Record W4407798776 · doi:10.1080/02701960.2025.2466199

The state of the academic pharmacy workforce specializing in geriatrics

2025· article· en· W4407798776 on OpenAlexaffabout
Marwa Noureldin, Antoinette B. Coe, Teresa DeLellis, Kalin M. Clifford, Carmen Freire‐Cobo, Ashley Campbell, Michael W. Nagy, Omolola A. Adeoye‐Olatunde, Manvi Sharma, Cheryl A Sadowski

Bibliographic record

VenueGerontology & Geriatrics Education · 2025
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGeriatricsPharmacyWorkforceMedicineFamily medicineMedical educationState (computer science)GerontologyPolitical sciencePsychiatryComputer science

Abstract

fetched live from OpenAlex

OBJECTIVE: To describe the training, career experiences, and roles and responsibilities of faculty members in American and Canadian schools/colleges of pharmacy involved in geriatrics-focused teaching, research, practice, or service. METHODS: A cross-sectional, web-based, self-administered survey was developed and pre-tested. Pharmacy faculty members with experience and/or expertise in geriatrics-focused practices or scholarships and/or who taught geriatrics-focused topics in US or Canadian pharmacy programs were eligible for participation. Participants were recruited using a multi-pronged approach between June and November 2022. FINDINGS: A total of 131 completed and non-duplicate surveys were received. Ninety percent of respondents were from US programs and 64.9% worked in public institutions. Sixty-two percent reported greater than 40% teaching efforts, and 39% indicated they were the only person in their program to advocate for geriatrics-focused content. Most reported expectations for scholarship (96.2%), and 77.1% maintained a clinical practice. Among those with research expectations, 53.5% agreed they had an adequate percentage allocation dedicated to research. CONCLUSION: Geriatrics pharmacy faculty report geriatrics and non-geriatrics teaching expectations, clinical practice workloads, and less time for scholarly productivity. Most respondents have extensive experience in geriatrics; however, many perceive themselves to be the only advocates for geriatrics-focused topics in their programs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.566
Threshold uncertainty score0.338

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.111
GPT teacher head0.450
Teacher spread0.339 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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