Mixing methods to understand engagement patterns during older adulthood
Bibliographic record
Abstract
Purpose: To explore ‘how’ and ‘why’ engagement patterns change throughout older adulthood. Methods: A convergent parallel mixed methods design was employed. Quantitative methods included 54 community-dwelling older adults (mean age = 79.17 years, age range = 65-97 years), 42 of which were included in the qualitative methods. Past and present weekly participation was documented in 30 activities (i.e., reading) on a four-point Likert scale (1 – never to 4 – often). Differences in participation were examined through a 3 (Age: 65-74 vs. 75-84 vs. 85+ years) x 2 (Time: past vs. present) x 4 (Activity Type: productive vs. social vs. passive vs. active) mixed ANOVA. Separate 3-way mixed ANOVAs were conducted within activity types to examine differences in specific activities. Qualitative fundamental description was used to understand ‘why’ engagement changes during older adulthood through (6) focus groups and (16) semi-structured interviews. Textual data was inductively analyzed for the emergence of themes through the constant comparison of meaning units. Results: Analyses revealed a decrease in productive participation with increased age (p = 0.003), and a decrease in specific productive pursuits within a five-year time frame: volunteer work (p = 0.033), care for others (p = 0.042), employment (p = 0.001), home repairs (p = 0.002), and heavy housework (p = 0.002). Similarly, a decrease in active leisure participation was reported over the previous five years (p = 0.005). No changes in social or passive leisure participation were identified. Explanations of engagement patterns were provided through qualitative analyses: (1) health, (2) death, (3) freedom, (4) desire, and (5) external factors. Conclusion: Older adults decrease productive and active leisure participation, while maintaining social and passive leisure activities. These patterns may reflect changes in health status, the death of social contacts/one’s spouse, the ‘freedom’ of later life, changes in desire, or various external influences. Acknowledgments: Social Sciences and Humanities Research Council
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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 | 0.028 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".