The Development and Validation of the Age-Based Rejection Sensitivity Questionnaire
Bibliographic record
Abstract
PURPOSE: There is much evidence suggesting that older adults are often negatively affected by aging stereotypes; however, no method to identify individual differences in vulnerability to these effects has yet been developed. The purpose of this study was to develop a reliable and valid questionnaire to measure individual differences in the tendency to anxiously expect, readily perceive, and intensely react to age-based rejection among older adults: the Age-Based Rejection Sensitivity Questionnaire (RSQ-Age). DESIGN AND METHODS: A pilot sample of older adults was asked to identify situations in which negative outcomes related to being an older adult might occur. From these responses, 58 scenarios representing 8 domains and themes were identified. Thirty initial items were created from this pool of scenarios, and this 30-item RSQ-Age underwent intensive testing and refinement to create the final 15-item RSQ-Age. The 15-item RSQ-Age was assessed for internal and test-retest reliability, as well as construct validity. RESULTS: Results revealed that the RSQ-Age has good internal (alpha = .91) and test-retest, r(72) = .74, p < .01, reliability and is a valid measure of age-based rejection sensitivity (RS). Construct validity was supported by correlations with personal RS, age-based stigma consciousness, self-consciousness, awareness of ageism, and self-esteem. IMPLICATIONS: The RSQ-Age is a useful measure for researchers and health care workers interested in the relationships between expectancy, perceptions, and reactions to age-based stigma and subsequent cognitive, behavioral, and health-related consequences.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".