L1-3Evidence based guidelines for newly arrived migrant and building migrant health networks
Bibliographic record
Abstract
This interactive workshop will take participants through a series of migrant health guidance case study debates. The cases will explore local adaptation of guidance for primary health care. Learning Objective: Participants will be able to list four new ECDC migrant guidance options. Participants will be able to describe four new delivery strategies. Participants will be invited to contribute to a network of migrant health practitioners. Evidence based guidelines become valuable when they are adapted and implemented for local contexts and primary healthcare. Implementation strategies must be pragmatic to adapt to the presence of different barriers and effect modifiers. The ECDC with the help of over 60 experts from across Europe, Canada, Australia, US have developed new GRADE guidance for testing and vaccination. The guidance considers ethics, values, a range of health conditions, including TB, HIV, Hepatitis B/C, Parasites and Vaccine Preventable Disease. Disease prevalence, gender, age and history of forced migration are used to increase the precision of guidance. The guidance also includes implementation considerations that include strategies to increase uptake of testing or vaccination and discuss linkage to care and treatment. This round table workshop will discuss migrant case studies and consider pragmatic implementation approaches from France, Holland and Canada.
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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 | 0.041 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.013 | 0.008 |
| Insufficient payload (model declined to judge) | 0.059 | 0.025 |
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".