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Record W4282917166 · doi:10.1017/s1463423622000226

Dementia-related continuing education for rural interprofessional primary health care in Saskatchewan, Canada: perceptions and needs of webinar participants

2022· article· en· W4282917166 on OpenAlexafffundabout
Julie Kosteniuk, Debra Morgan, Megan E. O’Connell, Dallas Seitz, Valerie Elliot, Melanie Bayly, Chelsie Cameron, Amanda Froehlich Chow

Bibliographic record

VenuePrimary Health Care Research & Development · 2022
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of CalgaryUniversity of Saskatchewan
FundersCanadian Institutes of Health ResearchSaskatchewan Health Research FoundationConsortium canadien en neurodégénérescence associée au vieillissement
KeywordsContinuing educationPerceptionDementiaNursingContinuing professional developmentMedical educationHealth professionalsPsychologyProfessional developmentPrimary careMedicineHealth careFamily medicinePolitical scienceDisease

Abstract

fetched live from OpenAlex

Dementia-related continuing education opportunities are important for rural primary health care (PHC) professionals given scarce specialized resources. This report explores the initial perceptions and continuing education needs of rural interprofessional memory clinic team members and other PHC professionals related to a short series of dementia-related education webinars. Three webinars on separate topics were delivered over an 8-month period in 2020 in Saskatchewan, Canada. The research design involved analysis of webinar comments and post-webinar survey data. Sixty-eight individuals participated in at least one webinar, and 46 surveys were completed. Rural memory clinic team members accounted for a minority of webinar participants and a majority of survey respondents. Initial perceptions were positive, with webinar topics and interactivity identified as the most effective aspects. Continuing education needs were mainly aligned with professional roles; however, some overlap of interests occurred. Future webinars will further explore learning needs within an interprofessional environment.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.363
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.031
GPT teacher head0.439
Teacher spread0.408 · 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 designQualitative
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

Citations7
Published2022
Admission routes3
Has abstractyes

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