Teaching the Role of Health Advocate: Reflections on two cross-cultural collaborative advocacy workshops for medical trainees and instructors in Oman
Bibliographic record
Abstract
In March 2014, medical educators from Canada and Oman collaborated to shape the foundation of health advocacy training in Oman. Using existing research and innovative tools, two workshops were developed, representing the first formalised approach to health advocacy for medical trainees in Oman. The development and application of the workshops highlighted many unique challenges and opportunities in advocacy training. This article summarises the process of developing and implementing the workshops as well as feedback from the participants and short-term consequences. Furthermore, this article seeks to explore the complexities of designing a cross-cultural curriculum. In particular, it reflects on how the role of health advocate may be perceived differently in various cultural and societal settings. Understanding and adapting to these influences is paramount to creating a successful health advocacy curriculum that is relevant to learners and responsive to the communities in which they work.
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 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 | 0.038 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.049 | 0.016 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.012 | 0.022 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".