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Record W4321599717 · doi:10.1007/s40670-022-01704-9

A Proposed Curricular Framework for an Interprofessional Approach to Deprescribing

2023· article· en· W4321599717 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMedical Science Educator · 2023
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsLunenfeld-Tanenbaum Research InstituteUniversité de MontréalOntario Tech UniversityCustom Security Industries (Canada)Université LavalUniversity of ReginaOntario Drug Policy Research NetworkWomen's College HospitalUniversity of TorontoPublic Health OntarioOntario Shores Centre for Mental Health SciencesManitoba Beekeepers' AssociationUniversity of OttawaDalhousie UniversityBridgepoint Active HealthcareUniversity of AlbertaInstitut Universitaire de Gériatrie de MontréalTrillium Health CentreUniversity of ManitobaMcMaster UniversityOntario Stroke NetworkCanadian Association on GerontologyBruyèreUniversity of Waterloo
FundersCanadian Institutes of Health Research
KeywordsDeprescribingInterprofessional educationMedical educationPsychologyMedicinePolypharmacyHealth careIntensive care medicinePolitical science

Abstract

fetched live from OpenAlex

Deprescribing involves reducing or stopping medications that are causing more harm than good or are no longer needed. It is an important approach to managing polypharmacy, yet healthcare professionals identify many barriers. We present a proposed pre-licensure competency framework that describes essential knowledge, teaching strategies, and assessment protocols to promote interprofessional deprescribing skills. The framework considers how to involve patients and care partners in deprescribing decisions. An action plan and example curriculum mapping exercise are included to help educators assess their curricula, and select and implement these concepts and strategies within their programs to ensure learners graduate with competencies to manage increasingly complex medication regimens as people age. Supplementary Information: The online version contains supplementary material available at 10.1007/s40670-022-01704-9.

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.

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)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.359
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.208
GPT teacher head0.503
Teacher spread0.295 · 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