Challenges, success factors and pitfalls: implementation of distributed medical education
Bibliographic record
Abstract
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".