Measuring Social Participation in the Health and Retirement Study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Large data sets have the potential to reveal useful information regarding social participation; however, most data sets measure social participation via individual items without a global assessment of social participation. RESEARCH DESIGN AND METHODS: We used data from the Health and Retirement Study (HRS) to assess whether 8 items from questionnaire pertaining to social participation (religious attendance, caring for an adult, activities with grandchildren, volunteering, charity work, education, social clubs, nonreligious organizations) formed a reliable, cohesive scale and to explore the predictive validity of this scale. We included respondents 65 years and older in the HRS who returned the psychosocial questionnaire in 2010 and 2012 with responses to the social participation items (n = 4,317 and n = 3,978). Three scales were explored: SoPart-30 using the original scoring; SoPart-10 using modified scoring; and SoPart-5 using dichotomous scoring. RESULTS: Five items were retained as a single factor for each scale, and graded response models and Mokken scale analysis confirmed the scale items with the SoPart-10 scale having the highest reliability (alpha = 0.74). DISCUSSION AND IMPLICATIONS: 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 modifiable factors that can promote it 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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".