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Record W4290973280 · doi:10.54656/jces.v15i1.447

Using Community Engagement to Initiate Conversations About Medication Management and Deprescribing in Primary Care

2022· article· en· W4290973280 on OpenAlexaffabout
Emily Galley, Barbara Farrell, James Conklin, Pam Howell, Lisa McCarthy, Lalitha Raman‐Wilms

Bibliographic record

VenueJournal of Community Engagement and Scholarship · 2022
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPolypharmacyDeprescribingMedicineParticipatory action researchBeers CriteriaCommunity-based participatory researchHealth carePublic healthNursingMedication therapy managementPharmacistSociologyIntensive care medicine

Abstract

fetched live from OpenAlex

Polypharmacy, or the simultaneous use of multiple medications, represents a significant public health challenge—particularly among older adults, who are more likely to experience negative clinical outcomes attributable to adverse reactions to or interactions between their medications (Canadian Institute for Health Information, 2013). Improved medication management on the part of both patients and health care providers (HCPs) is needed to address the issues and consequences associated with polypharmacy, but conversations between patients and their HCPs about options for medication changes remain the exception. In a rural community near Ottawa, Ontario, a community-based participatory research (CBPR) approach aimed to support improved public awareness of and participation in medication management and deprescribing through educational events aimed at older adults. This paper describes the processes researchers used in collaboration with community members to discuss and address medication management in a locally relevant manner, details the results of these processes, and suggests how similar approaches may be employed to empower patients and communities to address issues of personal 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)0.009
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.004
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 head0.466
GPT teacher head0.430
Teacher spread0.035 · 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.

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

Citations8
Published2022
Admission routes2
Has abstractyes

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