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Record W4409568313 · doi:10.26633/rpsp.2025.35

HEARTS Pharmacy: A framework for integrating pharmacists in hypertension and cardiovascular disease risk management in primary care

2025· article· en· W4409568313 on OpenAlexaff
Emily Ridley, Donald J. DiPette, Stephanie C. Gysel, Andrés Rosende, Norm R.C. Campbell, Carolina Neira Ojeda, Ricardo Pesenti, Vilma Irazola, Ricardo Humberto Ruano Arévalo, Pedro Ordúñez

Bibliographic record

VenueRevista Panamericana de Salud Pública · 2025
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsUniversity of CalgaryAtlantic Canada Opportunities Agency
Fundersnot available
KeywordsPharmacyMedicinePharmacistPharmacy practiceClinical pharmacyHealth careNursingDisease managementScope (computer science)Psychological interventionScope of practiceFamily medicineAlternative medicineHealth management systemPolitical science

Abstract

fetched live from OpenAlex

HEARTS Pharmacy, a project within the HEARTS in the Americas Initiative, provides a framework to integrate pharmacists into primary health care. Pharmacists are highly respected in health care but face challenges, such as limited scope of practice, regulatory barriers, and insufficient recognition, compounded by social norms that hinder their full potential. This paper presents compelling evidence that pharmacist-led interventions improve blood pressure control, lower cardiovascular risk, and reduce health care costs. It underscores the role of national pharmacy systems in ensuring access to high-quality medications. HEARTS Pharmacy emphasizes the role pharmacists play in team-based care, highlighting their expertise in medication management, patient education, and adherence. This paper advocates policy changes that empower pharmacists with greater responsibility, enabling them to play an active role in patient care. It also recommends actions to fully integrate pharmacists into care teams, positioning them as key players in hypertension control and cardio-vascular disease risk management within primary health care.

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)0.000
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 score0.607
Threshold uncertainty score0.864

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.057
GPT teacher head0.379
Teacher spread0.322 · 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

Citations6
Published2025
Admission routes1
Has abstractyes

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Same venueRevista Panamericana de Salud PúblicaSame topicPharmaceutical Practices and Patient OutcomesFrench-language works237,207