Evidence for the value of health promotion interventions in natural disaster management: Table 1:
Bibliographic record
Abstract
A rapid review of literature was conducted to identify effective health promotion (HP) intervention strategies that relate to the management of disasters from natural hazards, including prevention, preparedness, response and recovery measures. Searches were conducted in formal literature from 2000 to 2011 and then updated to 2013. Out of 719 relevant abstracts, 57 studies were selected for more detailed review. In total, 16 studies were annotated for the narrative synthesis; these articles all reported an outcome-oriented evaluation of an HP-related intervention in a natural disaster situation in low- and middle-income countries (LMIC) or vulnerable populations in high-income countries (HIC). These 16 studies were also assessed for quality of their evaluation design. Although it was not possible to select only strong study designs, LMIC weak designs were matched with stronger designs in HIC most of the time. A narrative synthesis was conducted to report the results. In the preparedness and mitigation stages, there were six articles referring to four HP strategies. In the response and recovery phases, there were 10 articles referring to an additional four HP strategies. HP plays a role in regaining a sense of control after disaster through: engaging victims of disaster in group decisions (including children), collaboration and networking, recognition of local strengths and assets, conducting community needs assessments, respecting local knowledge, training local resources as part of an ongoing system and use of pre-existing community focal points or organizations as trusted locations for community services and reconnections.
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 | 0.024 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 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".