Prioritizing the Prevention of Child-Family Separation: The Value of a Public Health Approach to Measurement and Action
Bibliographic record
Abstract
Disaster-affected children are among the most vulnerable populations and face a wide range of threats to their health and wellbeing. One of the most significant risks to children is separation from their family, a problem that occurs in most humanitarian contexts. Because separation can have lasting adverse consequences for children’s health and wellbeing, child protection actors frequently develop programs to respond to the needs of separated children. However, methods to measure prevalence, characteristics, and root causes of separation are scarce and rarely deployed in humanitarian settings. Existing measurement and programmatic approaches focus primarily on responding to already separated children and give little attention to the prevention of separation at a population level, the context and prevalence of separation, and the root causes of separation. Analyzing how a public health approach helps to fill these gaps, this paper presents a systematic, conceptual and practical case for incorporating a public health approach in the measurement of and programming for separation of children in humanitarian settings. It argues that a population-level, preventive approach to measurement and programming will complement the more common case-based, responsive approach to separation of children and enables children’s well-being amidst adversity.
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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 | 0.097 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.012 | 0.033 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".