Responding to Increasing Health and Social Needs of Unprotected Unaccompanied Minors in Paris in the Context of COVID-19: A Mixed Methods Case Study
Bibliographic record
Abstract
Unaccompanied minors (UMs) are children under the age of 18 who settle in a foreign country without a legal representative. In France, many UMs are left unprotected from child welfare services because assessment systems evaluate that they are not minors. In Paris, the non-governmental organization Médecins du Monde (MdM) offers unprotected UMs medical, psychological, and social care. In March 2020, the lockdown policy to contain COVID-19 constrained MdM to adapt its care provision model. This case study sought to answer the following question: how did volunteers and employees of MdM respond to the social and health needs of unprotected UMs during the spring 2020 lockdown in Paris? We analyzed a cohort of 58 UMs for eight weeks of lockdown using secondary quantitative data. We further explored the UMs’ needs and the experience of phone consultations, through 15 interviews with MdM’s program volunteers and employees. Time series showed a steady increase in UMs’ needs. The program’s adapted care provision likely contributed to reducing UMs’ feeling of isolation. It also had several negative consequences for unprotected UMs, volunteers, employees, and Médecins du Monde’s institution. This study highlights the role of non-governmental organizations in providing a particularly vulnerable migrant population – unprotected UMs – care and support, despite operational challenges in crisis times.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".