Bibliographic record
Abstract
INTRODUCTION: Although respecting older people was traditionally considered commonplace in family and society, getting older in modern Korea is often associated with unpleasant experiences of personal and institutional age-related discrimination, commonly known as ‘ageism'. This study aims to examine how Korean elders experienced types of Ageism and how this conversely relates to mental, physical, and self-rated health. METHODS: Data was gathered from a clustered sample of 638 people aged 60–89 via face-to-face interviews. Ageism was measured using a 20-item questionnaire from ‘The Ageism Survey' by Palmore. An exploratory factor analysis was performed to classify various types of Ageism. Health outcomes included depressive symptoms for mental health, using CES-D 20 Questions. Physician-diagnosed hypertension and self-rated health were also recorded. RESULTS: Of the 20 Ageism items, the results yielded four factors: ignorance ( α =.76), stereotype ( α =.80), employment ( α =.74), and healthcare ( α =.63). Gender, marital status, education, and residence were found to account for some variances in types of Ageism. In the effect of four types of ageism on health, three types— ignorance, stereotype, and health care —were all positively related to depressive symptoms. Only employment-related ageism increased the risk of developing hypertension, whereas healthcare-related ageism was significantly associated with poor self-rated health.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".