Internet-Delivered Early Interventions for Individuals Exposed to Traumatic Events: Systematic Review
Bibliographic record
Abstract
BACKGROUND: Over 75% of individuals are exposed to a traumatic event, and a substantial minority goes on to experience mental health problems that can be chronic and pernicious in their lifetime. Early interventions show promise for preventing trauma following psychopathology; however, a face-to-face intervention can be costly, and there are many barriers to accessing this format of care. OBJECTIVE: The aim of this study was to systematically review studies of internet-delivered early interventions for trauma-exposed individuals. METHODS: A literature search was conducted in PsycINFO and PubMed for papers published between 1991 and 2017. Papers were included if the following criteria were met: (1) an internet-based intervention was described and applied to individuals exposed to a traumatic event; (2) the authors stated that the intervention was intended to be applied early following trauma exposure or as a preventive intervention; and (3) data on mental health symptoms at pre-and postintervention were described (regardless of whether these were primary outcomes). Methodological quality of included studies was assessed using the Downs and Black checklist. RESULTS: The interventions in the 7 studies identified were categorized as selected (ie, delivered to an entire sample after trauma regardless of psychopathology symptoms) or indicated (ie, delivered to those endorsing some level of posttraumatic distress). Selected interventions did not produce significant symptom improvement compared with treatment-as-usual or no intervention control groups. However, indicated interventions yielded significant improvements over other active control conditions on mental health outcomes. CONCLUSIONS: Consistent with the notion that many experience natural recovery following trauma, results imply that indicated early internet-delivered interventions hold the most promise in future prevention efforts. More studies that use rigorous methods and clearly defined outcomes are needed to evaluate the efficacy of early internet-delivered interventions. Moreover, basic research on risk and resilience factors following trauma exposure is necessary to inform indicated internet-delivered interventions.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".