Prediction and Understanding of Resilience in Albertan Families: Longitudinal Study of Disaster Responses (PURLS) – Protocol
Bibliographic record
Abstract
Exposure to a natural disaster in childhood can have serious, long-lasting consequences, impacting physical and mental health, development, and learning. Although many children experience negative effects after a disaster, the majority do not, and what differentiates these groups is not well understood. Some of the factors that influence disaster-related outcomes in the midst of adversity include parents' mental health, the home environment, and socioeconomic status. Furthermore, genetics has also a role to play in how children respond to stressors. We had the opportunity to conduct a natural experiment of disaster recovery following the Alberta 2013 Flood. This paper presents the detailed protocol on prediction of resilience in Albertan families, and validation with cortisol data. In addition, data collection procedures, developing resiliency screening tools, candidate gene identification, genotyping, DNA methylation, and genomic analyses are described to achieve the research objectives. This study produced new knowledge by using pre- and post-disaster information on children's health and development, including children's genetics and responses to stress. This information has been identified as important to governments and other organizations invested in early child development. Our comprehensive research plan generates evidence that can be mobilized population-based approaches to improve child and family resiliency.
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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 | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".