ANA Light Field Hospital: A New Model of Civilian Cooperation and Response during Disasters, Emergencies in Austere Environments, Italy
Bibliographic record
Abstract
Study/Objective: The objective of this field research study is to advance learning to improve coordination and service-delivery to crisis-affected populations; highlighted by Canadian Red Cross (CRC) engagement with Syrian populations along the continuum-of-care from Syria to Canada.Background: Syria is the biggest humanitarian and refugee crisis of our time.Per the United Nations High Commissioner for Refugees (UNHCR), 4.8 million Syrians have fled to surrounding nations, and 6.6 million are internally-displaced.In 2015, the Canadian Prime Minister-elect pledged to bring 25,000 Syrian refugees to Canada.CRC deployed technical personnel along the entire migration journey: Jordan, the Mediterranean Sea, Greece, Germany, and Canada.Service-delivery coordinated by CRC included clinical health, referral, Psychological Support Services (PSS), Restoring-Family-Links (RFL) protection, transportation, lodging and other services.Methods: End of Mission (EOM) reports (n = 8) were analyzed.CRC Syria Response Evaluation was reviewed, which included key informant interviews (n = 24), focus groups (n = 125 participants), and a survey of volunteers (n = 583).Based on this data and operational experience we have identified recommendations.The EOMs and Response Evaluation will be used to develop an informed set of questions to panel members who can speak to their extensive experience involved in the response; including deployed technical personnel, a Syrian who journeyed from Syria to Canada, and frontline service-providers.Results: Approximately 42% of the refugees arriving in Canada were assisted by CRC.Challenges included coordination, providing basic-health and PSS during migration, and systemnavigation and referral upon arrival to Canada.Engaging at various points along the migration journey provided unique opportunities for RFL.Recommendations arising from both successes and challenges included: ensuring human-resource systems are prepared; increasing focus on managing health, including child PSS; and using international experience to improve reintegration services.Conclusion: Knowledge generated from this response models challenges and solutions in supporting service-delivery and coordination with populations affected by crisis throughout the migration journey.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.040 | 0.008 |
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".