ENHANCING SUICIDE RISK ASSESSMENT WITH A BRIEF VERSION OF THE REASONS FOR LIVING SCALE-OLDER ADULT VERSION (RFL-OA)
Bibliographic record
Abstract
Older adults have high rates of suicide and employ highly lethal means of self-harm (WHO, 2014). The aging of the population necessitates tools that quickly identify suicide risk and resiliency processes (Heisel & Duberstein, 2016). Linehan and colleagues (1983) initially identified Reasons for Living (RFL) as an adaptive psychological construct potentially preventive of suicide thoughts and behavior. Age-specific RFL scales have since been developed, including for older adults. The purpose of the present study was to develop and evaluate a brief version of the Reasons for Living Scale-Older Adults version (RFL-OA; Edelstein et al., 2009) for use in clinical and research contexts. A series of secondary analyses was conducted of a combined dataset (N=204) derived from three studies of late-life suicide ideation (Heisel & Flett, 2006; Heisel et al., 2015; Heisel, Neufeld, & Flett, 2016). We specifically assessed RFL-OA item distributions, and their contribution to internal consistency, construct validity, and social desirability. Thirty RFL-OA items were significantly associated with lifetime history of suicide attempt. Of these, 13 items were also associated with current suicide ideation. No item was highly correlated with social desirability. Findings supported the internal consistency, test-retest reliability, and construct and criterion validity of this abbreviated RFL scale. This study’s findings support the reliability and validity of a 13- item Reasons for Living-Suicide Risk scale (RFL-SR) for use with clinical and/or community-residing samples of older adults. These findings suggest promise for this abbreviated measure in assessing a psychological construct potentially protective against later-life suicide risk.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".