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Record W4415263026 · doi:10.1161/hyp.82.suppl_1.tac244

Abstract TAC244: Identifying Gaps in Hypertension Training Across Canadian Health Professions: A National Survey

2025· article· en· W4415263026 on OpenAlexaffabout
Isaac Glassman, Certina Ho, Laurel Taylor, Ross T. Tsuyuki, Norman R.C. Campbell, Sachin Vidur Pasricha

Bibliographic record

VenueHypertension · 2025
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsUniversity of CalgaryUniversity of AlbertaWestern UniversityCanadian Transplant AssociationCanadian Political Science AssociationCanadian Wildlife FederationCanadian Medical AssociationCanadian Federation of University WomenCovenant HealthUniversity of TorontoUniversity of Ottawa
Fundersnot available
KeywordsAccreditationGuidelinePrimary careConsistency (knowledge bases)CurriculumMultidisciplinary approachMEDLINEHealth careDisease

Abstract

fetched live from OpenAlex

Introduction: Hypertension is the most prevalent cardiovascular risk factor in Canada and a major driver of morbidity. Despite its importance, no national standard exists for how hypertension is taught across professions. The extent and consistency of hypertension education in Canadian healthcare training programs remain unclear. Hypothesis: We hypothesized that hypertension education across Canadian health professions is variable, with gaps in practical skills, guideline adherence, and interprofessional care. Methods: We conducted a cross-sectional national survey (March–April 2025), distributed via trainee representatives of healthcare associations. All accredited Canadian MD, NP, PharmD, RN/RPN, and family medicine programs were eligible. The 22-item questionnaire assessed seven domains, including curriculum structure, hypertension pathophysiology, BP measurement, risk assessment, and team-based care. Responses were selected from predefined options and mapped post hoc to a 4-point scale, with scores ≥3 considered adequate. The primary outcome was the proportion of programs meeting this threshold in each domain. Items also asked students to list secondary hypertension causes, treatment options, and BP measurement steps. Programs reported total instruction hours. Data were analyzed descriptively, stratified by profession, and grouped by institution, within an implementation science lens. Ethics approval was obtained from the University of Toronto REB (#47478). Results: Sixty-three programs responded (60% response rate): 10 MD, 10 PharmD, 10 family medicine, 32 RN/RPN, and one NP. Most programs adequately covered hypertension pathophysiology and cardiovascular risk assessment but multidisciplinary care and hands-on skills were inconsistently addressed. Only 30% reported adequate training in guideline-based BP measurement. PharmD, MD, and RN students correctly identified a mean of 4.2, 3.75, and 3.2 of six BP measurement steps, respectively. Fewer than 30% of programs met benchmarks for use of digital tools or adherence counseling. Only 30% of MD programs reported adequate team-based care training, compared to 100% of RPN/LPN and 60% of RN and PharmD programs. Instructional hours ranged from 66.8 in MD to 9.5 in RPN/LPN programs. Conclusions: Hypertension education across Canadian healthcare training programs is variable, with notable gaps in hands-on skills. These findings establish a baseline to guide standardized interprofessional hypertension training development.

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.997
Threshold uncertainty score0.169

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.007
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.275
GPT teacher head0.481
Teacher spread0.206 · 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.

Study designObservational
DomainMethods
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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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