Web-Based Psychosocial Interventions for Disaster-Related Distress: What Has Been Trialed in the Past, and What Can We Learn From This?
Bibliographic record
Abstract
OBJECTIVES: To summarize reports describing implementation and evaluation of Web-based psychosocial interventions for disaster-related distress with suggestions for future intervention and research, and to determine whether a systematic literature review on the topic is warranted. METHODS: Systematic searches of Embase, PsycINFO, and MEDLINE were conducted. Duplicate entries were removed. Two rounds of inclusion/exclusion were conducted (abstract and full-text review). Relevant data were systematically charted by 2 reviewers. RESULTS: The initial search identified 112 reports. Six reports, describing and evaluating 5 interventions, were included in a data analysis. Four of the 5 interventions were asynchronous and self-guided modular programs, with interactive components. The fifth was a short-term, online supportive group intervention. Studies utilized a variety of evaluation methods, and only 1 of 14 outcome measures used across the studies was utilized in more than 1 project. CONCLUSIONS: Several Web-based psychosocial interventions have been developed to target disaster-related distress, but few programs have been formally evaluated. A systematic review of the topic would not be recommended at this time due to heterogeneity in reported studies. Further research on factors impacting participation, generalizability, and methods of program delivery with consistent outcome measures is needed.
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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 | 0.019 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".