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Record W2778406512 · doi:10.1111/ijpp.12422

Community pharmacists’ perspectives on shared decision-making in diabetes management

2017· article· en· W2778406512 on OpenAlexafffundabout
Zahava R. S. Rosenberg-Yunger, Lee Verweel, Michael R. Gionfriddo, Lori MacCallum, Lisa Dolovich

Bibliographic record

VenueInternational Journal of Pharmacy Practice · 2017
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsMcMaster UniversityCanadian Pharmacists AssociationUniversity of WaterlooToronto Metropolitan UniversityUniversity of TorontoImpact
FundersGovernment of Ontario
KeywordsMedicineCommunity pharmacyDiabetes managementDiabetes mellitusFamily medicineNursingPharmacyType 2 diabetesEndocrinology

Abstract

fetched live from OpenAlex

OBJECTIVES: Shared decision-making (SDM) is an approach where patients and clinicians share evidence and patients are supported to deliberate options resulting in preference-based informed decisions. The aim of this study was to describe community pharmacists' perceptions and awareness of SDM within their provision of general diabetes management [including Ontario's MedsCheck for Diabetes (MCD) programme], and potential challenges of implementing SDM within community pharmacy. METHODS: This qualitative study used semistructured interviews with a convenience sample of community pharmacists. Data were analysed using thematic analysis. KEY FINDINGS: We conducted 16 interviews. Six participants were male, and nine were certified diabetes educators. When providing a MCD, participants used aspects of a patient-centred approach focusing on providing education. Variation was evident in participants' description and use of SDM, as well as in their perceived level of training in SDM. Participants also highlighted challenges surrounding implementing a SDM approach in practice. CONCLUSION: Pharmacists are well positioned to apply SDM within community settings; however, implementation barriers exist. Pharmacists will require additional training as well as perceived patient and physician barriers should be addressed to encourage uptake.

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)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.324
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0030.001
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.282
GPT teacher head0.570
Teacher spread0.287 · 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

Citations14
Published2017
Admission routes3
Has abstractyes

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