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Record W3159444191 · doi:10.1093/ijpp/riab023

Pharmacist contributions to patient care and medical conditions present among recipients of Ontario primary care team pharmacist-led medication reviews: a qualitative analysis

2021· article· en· W3159444191 on OpenAlexafffundabout
Sara Rezahi, Annalise Mathers, Nichelle Benny Gerard, Kei Cheng Mak, Lisa Dolovich

Bibliographic record

VenueInternational Journal of Pharmacy Practice · 2021
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsUniversity of TorontoUniversity of Waterloo
FundersGovernment of Ontario
KeywordsMedicinePharmacistPrimary careFamily medicineQualitative researchNursingHealth careChronic diseaseDisease managementPharmacyAlternative medicineHealth management system

Abstract

fetched live from OpenAlex

OBJECTIVES: Family Health Teams (FHTs) in Ontario, Canada are interdisciplinary primary healthcare practices where pharmacists engage in patient care including medication and chronic disease management. METHODS: Descriptive content analysis was used to examine qualitative responses of FHT pharmacists on their most significant contribution to a patient's medication management. KEY FINDINGS: Common roles described included medication management (70.2%), counselling and education (15.5%), monitoring and optimization (11.3%) and administration (3.1%). Chronic conditions addressed were diabetes (39.0%), cardiovascular (22.0%), pain (17.0%) and mental health (11.0%). CONCLUSIONS: While FHT pharmacists primarily view themselves as medication management experts, larger roles in counselling, education and chronic disease management are key contributions.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.137
Threshold uncertainty score0.273

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.097
GPT teacher head0.528
Teacher spread0.431 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations3
Published2021
Admission routes3
Has abstractyes

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