Development and Validation of the Elderly Suicide Screening Scale
Bibliographic record
Abstract
Background This study stems from the need to develop and validate a tool for interprofessional teams in Primary health care for screening for suicide among the elderly in the community. Objective To evaluate the psychometric properties of the Non-Institutionalized Elderly Suicide Screening Scale (Escala de Rastreamento de Renúncia à Vida no Idoso não institucionalizado - ERRVI). Methods This is a psychometric study focused on evaluating evidence of content validity and internal structure. The ERRVI construction process followed the guidelines for scale development based on theoretical models derived from qualitative research, which followed the methodological and theoretical approaches of grounded theory and symbolic interaction. The instrument underwent content validity evidence analysis carried out by a panel of experts, considering the Content Validity Index (CVR). After the pre-test, the internal structure was evaluated using Exploratory Factor Analysis (AFE). Reliability was assessed using three indicators: Cronbach's alpha, Omega, and ORION scores. It involved 300 elderly individuals from two municipalities in the central-western region of Sao Paulo, Brazil, and was conducted from September to November 2020. Results The final version comprised 31 items, categorized into four dimensions: autonomy, self-governance, self-care, and life satisfaction. The total explained variance was 50.82%, with factor loadings ranging from 0.31 to 0.86. The reliability indicators revealed a Cronbach’s alpha of 0.88, McDonald's Omega of 0.95, and scores for the dimensions assessed by the Overall Reliability of Fully-Informative Prior Oblique N-EAP (ORION) ranging from 0.78 to 0.84. Conclusion The ERRVI showed evidence of content validity and internal structure in accordance with the recommended psychometric parameters. This is an innovative tool with the social value, given the scarcity of tools that screen the risk of suicide among the elderly in the community. It is the first of its kind in Brazil and the third globally.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".