Exploring glocalization in the construction and implementation of global curricula.
Bibliographic record
Abstract
11020 Background: Despite proposed advantages of global oncology curricular harmonization including physician mobility and improving the quality of care the challenges and unintended consequences require greater study. The aim of this study was to problematize the concept and implementation of global oncology curricula and their relationship to local contexts of power and culture. Methods: Fourteen international participants involved in the development and implementation of global oncology curricula completed in-depth, one-on-one semi-structured interviews lasting 40-60 minutes. Snowball sampling was employed. The participant sample was representative of different geographic regions, genders and professional scopes of practice to ensure diverse perspectives were sought. Through iterative analyses, using an abductive approach, the study team discussed and reviewed the data and made revisions through collaborative analysis to enhance comprehensiveness and to improve credibility. In the final analysis the meaning and implication of the themes were discussed yielding a conceptual analysis. Results: Our data have articulated 5 key challenges for global curricula including 1) Ambiguous or conflicting perspectives on the purpose and scope of Global Oncology Curricula 2) Insufficient representation of diverse perspectives and realities in the creation of the final curricula 3) A rigid conceptualization of competency requirements 4) A mismatch between the curricular requirements and local context and 5) The influence of power relationships and decision makers. Leveraging the strengths of diversity including fostering representation, addressing power differentials and factoring local contexts may be an approach to mitigating these challenges. Conclusions: Global oncology curricula may serve important advocacy roles within the healthcare system. Leveraging diversity may positively impact the common challenges in the construction and implementation of global oncology curricula.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".