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Record W2897267042 · doi:10.1111/medu.13715

Challenges, success factors and pitfalls: implementation of distributed medical education

2018· article· en· W2897267042 on OpenAlexaffabout
Anurag Saxena, Kathy Lawrence, Loni Desanghere, Tom Smith‐Windsor, G. H. White, Dan Florizone, Sinead McGartland, Kent Stobart

Bibliographic record

VenueMedical Education · 2018
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsStakeholderCorporate governanceCritical success factorGovernment (linguistics)Focus groupMedical educationPacePublic relationsKnowledge managementPolitical sciencePsychologyMedicineBusinessComputer science

Abstract

fetched live from OpenAlex

OBJECTIVES: There are only a few descriptive reports on the implementation of distributed medical education (DME) and these provide accounts of successful implementation from the senior leadership perspective. In Saskatchewan, over a period of 4 years (2010-2014), four family medicine residency sites were established and two additional sites could not be developed. The aim of this study was to identify challenges, success factors and pitfalls in DME implementation based upon experiences of multiple stakeholders with both successful and unsuccessful outcomes. METHODS: Data were obtained through document analysis (n = 64, spanning 2009-2016; perspectives of government, senior leadership, management and learners), focus groups of management and operations personnel (n = 10) and interviews of senior leaders (n = 4). Challenges and success factors were ascertained through categorisation. Iterative coding guided by three sensitising frameworks was used to determine themes in organisational dynamics. RESULTS: Both challenges and success factors included contextual variables, governance, inter- and intra-organisational relationships (most common success factor), resources (most common challenge), the learning environment and pedagogy. Management and operations were only a challenge. Organisational themes affecting the outcome and the pitfalls included the pace of development across multiple sites, collaborative governance, continuity in senior leadership, operations alignment and reconciliation of competing goals. CONCLUSIONS: Emerging opportunities for DME can be leveraged through collaborative governance, aligned operations and resolution of competing goals, even in constrained contexts, to translate political will into success; however, there are pitfalls that need to be avoided. Our findings based upon multi-stakeholder perspectives add to the body of knowledge on deployment, carefully considering the conditions for success and associated pitfalls.

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)0.059
metaresearch head score (Gemma)0.119
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.941
Threshold uncertainty score0.313

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0590.119
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0060.006
Scholarly communication0.0100.008
Open science0.0040.012
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.410
Teacher spread0.388 · 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".

Quick stats

Citations18
Published2018
Admission routes2
Has abstractyes

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