Massacre, Earthquake, Flood
Bibliographic record
Abstract
Abstract. Natural (e.g., earthquake, flood, wildfires) and human-made (e.g., terrorism, civil strife) disasters are inevitable, can cause extensive disruption, and produce chronic and disabling psychological injuries leading to formal diagnoses (e.g., post-traumatic stress disorder [PTSD]). Following natural disasters of earthquake (Christchurch, Aotearoa/New Zealand, 2010–11) and flood (Calgary, Canada, 2013), controlled research showed statistically and clinically significant reductions in psychological distress for survivors who consumed minerals and vitamins (micronutrients) in the following months. Following a mass shooting in Christchurch (March 15, 2019), where a gunman entered mosques during Friday prayers and killed and injured many people, micronutrients were offered to survivors as a clinical service based on translational science principles and adapted to be culturally appropriate. In this first translational science study in the area of nutrition and disasters, clinical results were reported for 24 clients who completed the Impact of Event Scale – Revised (IES-R), the Depression Anxiety Stress Scales (DASS), and the Modified-Clinical Global Impression (M-CGI-I). The findings clearly replicated prior controlled research. The IES-R Cohen’s d ESs were 1.1 (earthquake), 1.2 (flood), and 1.13 (massacre). Effect sizes (ESs) for the DASS subscales were also consistently positive across all three events. The M-CGI-I identified 58% of the survivors as “responders” (i.e., self-reported as “much” to “very much” improved), in line with those reported in the earthquake (42%) and flood (57%) randomized controlled trials, and PTSD risk reduced from 75% to 17%. Given ease of use and large ESs, this evidence supports the routine use of micronutrients by disaster survivors as part of governmental response.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.004 |
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".