Health information for asylum seekers in transit centers in Norway
Bibliographic record
Abstract
Background Migration is a significant public health issue throughout the world due to large numbers of asylum seekers fleeing war, economic instability or political unrest. Essential needs of migrants include food, shelter, access to health and security as well as information. This study, commissioned by the Directorate of Immigration in Norway, presents findings from a report by the Norwegian Institute of Public Health and NAKMI investigating how health and access to health is communicated to newly arrived asylum seekers in Norway. Methods During February, 2017, semi-structured interviews were conducted with 24 asylum seekers, 9 health care providers, and 13 reception center employees at five transit centers in Norway. All interviews were tape recorded and conducted in Norwegian, English, Arabic or Tigrinya. Thematic areas addressed rights, the health care system, and health prevention. Results Preliminary findings note that health information in the transit phase is limited. Little systematic information are provided on rights, the health system, or health prevention or promotion. Focus is instead on access to urgent medical needs, hygiene, tuberculosis screening, vaccination, and basic health habits. Mental health, sleeping problems or trauma seem rarely to be addressed unless there is an acute situation. Coping mechanisms, nutrition or chronic conditions are not prioritized topics. Diversity, in terms of education, age, language, and expectations impact communication strategies. Conclusions Health information provided to asylum seekers in the transit phase has implications for how they understand, access, and navigate the health system and can influence health-seeking and health-promoting behaviors. The study findings will inform government authorities on how to better exploit opportunities to provide health information to asylum seekers. With increasing pressures of migration, it is critical to understand how to optimize health communication strategies. Key messages: The vital role of health communication to address health literacy has potential to facilitate and improve health seeking behaviors as well as support health promoting behaviors among asylum seekers. Understanding the communication landscape of asylum seekers has implications for improving information about rights, systems, and health promotion that can thereby enhance health of asylum seekers.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".