MétaCan
Menu
Back to cohort
Record W3197948781 · doi:10.51731/cjht.2021.102

Community Pharmacist–Led Medication Reviews

2021· article· en· W3197948781 on OpenAlexaboutno aff
Jonathan Harris, Charlene Argáez

Bibliographic record

VenueCanadian Journal of Health Technologies · 2021
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsPharmacistMedicinePharmacyContext (archaeology)Psychological interventionCommunity pharmacistNursingFamily medicineCommunity pharmacy

Abstract

fetched live from OpenAlex


 Community pharmacist–led medication reviews are widely used in Canada and internationally.
 It has been shown that community pharmacist–led medication reviews can identify medication issues. Broadly speaking, pharmacists feel qualified to deliver this service and, from the few studies that measured patient satisfaction, patients find value in receiving a medication review in a community pharmacy.
 In terms of patient and health system outcomes, community pharmacist–led medication reviews seem to have limited impact. Individuals living with defined chronic conditions, such as diabetes or hypertension, or those living with multiple chronic conditions seem most likely to benefit.
 No studies of cost-effectiveness in the Canadian context were identified.
 A variety of barriers that impact pharmacist-led medication reviews were identified in the literature, including:
 
 limited communication between community pharmacists and prescribers resulting in pharmacists’ recommendations not being implemented
 a lack of time on the part of pharmacists
 challenges with patient selection.
 
 
 Policy interventions that may help alleviate these barriers include:
 
 incentivizing communication and collaboration between pharmacists and prescribers
 reducing administrative burden
 improving access to patient information
 enhancing patient selection by incentivizing service provision for the most medically complex patients.
 
 

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.956
Threshold uncertainty score0.510

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.346
GPT teacher head0.483
Teacher spread0.137 · 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 designNot applicable
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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Health TechnologiesSame topicPharmaceutical Practices and Patient OutcomesFrench-language works237,207