109:poster Were forcibly displaced people prioritized in the COVID-19 national response plans?
Bibliographic record
Abstract
Background Forcibly displaced people represent a huge humanitarian problem globally. At the end of 2020, the total number was 82,4 million; from those, 34,4 million were refugees, asylum seekers, and Venezuelan displaced abroad. Forcibly displaced people were identified as priority populations during the pandemic due to their risk of being the last served populations with healthcare. This paper aimed to identify if this population was prioritized in the COVID-19 national response plans of a sample of 86 countries. Methods This study is part of a document analysis of 86 COVID-19 national response plans, assessing the degree of comply to quality parameters of effective priority setting. One of the parameters included was the degree to which vulnerable populations such as forcibly displaced people were explicitly prioritized for receiving COVID-19 related interventions or for continuity of non-COVID healthcare services. The analysis involved assessing whether and how forcibly displaced people were prioritized in the COVID-19 national response plans. This was compared with the displaced populations identified in the host countries’ UNHCR Forced Displacement 2020 report. Results Only five countries among 86 analyzed prioritized forcibly displaced people in their COVID-19 national response plans. Among the top ten forcibly displaced people hosting countries, Uganda was the only one with an explicit prioritization of this vulnerable group. Although Turkey, Colombia, and Germany account for nearly one-fifth (6,6 million) of refugees, asylum seekers and Venezuelans displaced abroad, none of the COVID-19 response plans of these countries prioritized these populations. Discussion Few countries recognized forcibly displaced people as a vulnerable population in their COVID-19 response and preparedness plans. Governments may have incorporated actions and interventions for these vulnerable groups after publishing the COVID-19 response plans. It would be essential to evaluate the impact of this lack of prioritization on the health and wellbeing of these population groups.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.116 | 0.014 |
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".