Internet-based Self-Assessment after the Tsunami: lessons learned
Bibliographic record
Abstract
BACKGROUND: In the aftermath of the Tsunami disaster in 2004, an online psychological self-assessment (ONSET) was developed and made available by the University of Zurich in order to provide an online screening instrument for Tsunami victims to test if they were traumatized and in need of mental health care. The objective of the study was to report the lessons learnt that were made using an Internet-based, self-screening instrument after a large-scale disaster and to discuss its outreach and usefulness. METHODS: Users of the online self-assessment decided after finishing the procedure whether their dataset could be used for quality control and scientific evaluation Their answers were stored anonymously only if they consented (which was the case in 88% of the sample), stratified analyses according to level of exposure were conducted. RESULTS: A total of 2,914 adult users gave their consent for analysis of the screenings. Almost three quarter of the sample filled out the ONSET questionnaire within the first four weeks. Forty-one percent of the users reported direct exposure to the Tsunami disaster. Users who were injured by the Tsunami and users who reported dead or injured family members showed the highest degree of PTSD symptoms. CONCLUSION: ONSET was used by a large number of subjects who thought to be affected by the catastrophe in order to help them decide if they needed to see a mental health professional. Furthermore, men more frequently accessed the instrument than women, indicating that Internet-based testing facilitates reaching out to a different group of people than "ordinary" public mental health strategies.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".