Development and Initial Validation of the Response to Suicidal Ideation Inventory (RSII)
Bibliographic record
Abstract
OBJECTIVES: Young adults (18-29 years old) often report difficulties coping during suicide crises. However, the responses that young adults employ to manage suicidal ideation (SI) are underexplored. This study developed a new measure to quantify how young adults with a history of suicidal thoughts and behaviors (STBs) respond to their SI, and conducted an initial evaluation of the measure's psychometric properties. METHODS: = 3.3, 18-29 years old), completed the RSII, as well as questionnaires assessing the RSII's content validity, general coping, emotion dysregulation, history of STBs, reasons for living, and future expectations of engaging in STBs. RESULTS: Results from an exploratory factor analysis indicated that a 43-item, seven-factor solution was an appropriate fit to the data. The RSII and its subscales showed acceptable reliability, as well as preliminary content validity. The RSII's subscales also demonstrated medium-sized positive correlations with measures of general coping, and weak associations with emotion dysregulation, suicide resilience, and expectations of future STBs. CONCLUSIONS: Our results point to some important directions for refining the RSII. Exploring the range and types of responses young adults engage in to manage their SI may improve our understanding of, and ability to predict, fluctuations in SI severity.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".