Suicide exposure experience screener for use in therapeutic settings: A validation report
Bibliographic record
Abstract
INTRODUCTION: A brief screener assessing experience of exposure to suicide for use in therapeutic settings is warranted. To examine the concurrent validity of such a screening tool, labeled as the Suicide Exposure Experience Screener (SEES), the associations of the two SEES items: (i) reported closeness with the person who died by suicide and (ii) perceived impact of suicide death with psychological distress are presented. METHODS: = 7782) were used to provide evidence of concurrent validity of closeness and impact of suicide exposure. RESULTS: Overall, closeness and impact were significantly correlated with measures of global distress across five different datasets, showing small to medium effect sizes. Closeness and impact were also intercorrelated demonstrating a large effect size across all surveys. This report used cross-sectional data and comprised varied sample sizes across different datasets that influenced statistical significance of obtained effects and did not tease apart the roles of cumulative exposure of suicide and prolonged bereavement in experiencing global distress. CONCLUSION: The SEES has clinical utility in determining psychological distress in bereaved individuals and is recommended for use in therapeutic settings.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".