Hair hormone data from Syrian refugee children: Perspectives from a two-year longitudinal study
Bibliographic record
Abstract
For numerous issues of convenience and acceptability, hair hormone data have been increasingly incorporated in the field of war trauma and forced displacement, allowing retrospective examination of several biological metrics thought to covary with refugees’ mental health. As a relatively new research method, however, there remain several complexities and uncertainties surrounding the use of hair hormones, from initial hair sampling to final statistical analysis, many of which are underappreciated in the extant literature, and restrict the potential utility of hair hormones. To promote awareness, we provide a narrative overview of our experiences collecting and analyzing hair hormone data in a large cohort of Syrian refugee children (n = 1594), across two sampling waves spaced 12 months apart. We highlight both the challenges faced, and the promising results obtained thus far, and draw comparisons to other prominent studies in this field. Recommendations are provided to future researchers, with emphasis on longitudinal study designs, thorough collection and reporting of hair-related variables, and careful adherence to current laboratory guidelines and practices.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".