LEVERAGING LARGE DATA: ASSESSING SOCIAL PARTICIPATION IN THE HEALTH AND RETIREMENT STUDY
Bibliographic record
Abstract
Continued participation in social activities is associated with positive health outcomes and higher quality of life among community dwelling older adults. Large datasets have the potential to reveal useful information regarding social participation and the factors that impact it; however, most datasets measure social participation via individual items. We use data from the Health and Retirement Study (HRS) to assess whether the five items from the psychosocial questionnaire pertaining to social participation (volunteer with youth, charity work, education, social clubs, non-religious organizations) form a reliable, cohesive scale. The psychosocial questionnaire is administered to an alternating subsample in each wave of the HRS since 2008. We included respondents 65 and older who returned the psychosocial questionnaire in 2010 and 2012 with responses to the social participation items (n=4,317 and n=3,978). In order to test whether the social participation items formed a reliable and cohesive scale, we first used the data from the 2010 wave and performed exploratory factor analysis (EFA) of the social participation items. All five items loaded onto a single factor (i.e., Eigen value >1.0) and had borderline reliability (Cronbach’s alpha= 0.68). We then used the 2012 sample and performed a confirmatory factor analysis (CFA) on the five items using a structural equation model. The CFA showed reasonable fit (RMSEA = 0.04, CFI = 0.99). Results suggest that a scale derived from the social participation items in the HRS may be useful in characterizing general social participation levels and identifying factors that can promote social participation in older populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".