Enhancing the Assessment of Resiliency to Suicide Ideation among Older Adults: The Development and Initial Validation of the Reasons for Living-Suicide Resiliency Scale (RFL-SR)
Bibliographic record
Abstract
Objectives: To derive a brief late-life suicide resiliency scale from the 69-item Reasons for Living Scale-Older Adult version (RFL-OA).Methods: We conducted a series of secondary analyses of RFL-OA data (N = 204) from a dataset combining: 1. A follow-up assessment of nursing home residents in the Geriatric Suicide Ideation Scale (GSIS) development study; 2. A trial of Interpersonal Psychotherapy (IPT) with suicidal older adults; 3. A longitudinal study of risk and resiliency to late-life suicide ideation. We specifically assessed the distributions of RFL-OA items and their associations with suicide ideation and behavior to create an RFL-Suicide Resiliency subscale (RFL-SR); we then tested the psychometric properties of this measure’s items drawn from the larger RFL-OA.Results: Nine RFL-OA items were significantly associated with suicide ideation and history of suicide behavior and were not highly correlated with social desirability. Psychometric analyses supported the internal consistency, test–retest reliability, and construct validity of this scale.Conclusions: The items of the RFL-SR demonstrated strong psychometric properties with older adults in clinical and community settings.Clinical Implications: The RFL-SR may make a useful addition to suicide risk assessment in gerontological research and clinical practice.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".