Development and validation of a scale to assess the belief that ‘age causes illness’
Bibliographic record
Abstract
Objectives Self-directed ageism is the application of stereotypic age-related beliefs to oneself, and is known to negatively impact health-related motivation (Levy, Citation2003; Citation2022). This study focused on the specific self-directed stereotype that ‘age causes illness’ and aimed to develop and test a multi-item measure to assess this implicit, limiting belief.Methods and Measures Survey data was collected from N = 347 adults in southeastern Idaho (ages 45–65 years old, 60% female). A variety of measures were used to assess the discriminant, convergent and predictive validity of the Age Causes Illness scale including: socio-demographics (age, sex, education), psychosocial resources (personality, optimism, social support, depressive symptoms), health/aging expectations, and indicators of physical health.Results The seven-item Age Causes Illness scale is reliable and shows an expected pattern of discriminant and convergent correlations with relevant socio-demographic, psychosocial, and aging-related measures. The belief that ‘age causes illness,’ as assessed with this new scale, is related to both objective and subjective indicators of physical health.Conclusions The Age Causes Illness scale is a brief screening tool, potentially applicable in behavioral health settings as an initial step toward discussion of the implicit, and often unchallenged, belief that age alone determines the onset, progression, and offset of illness.
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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 | 0.010 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".