Frequency of participation is a good proxy for engagement in a model of successful aging
Bibliographic record
Abstract
Traditionally the successful aging (SA) literature has suggested that engagement with life is comprised of both interpersonal relationships and productive activities (Rowe & Kahn, 1998). Recent work by Liffiton & Weir (2012) suggests that the frequency of participation may be a valid measure of engagement. To compare these, the current study examined 181 community dwelling older adults (mean age = 67.4; 75 male/106 female). An activity profile, an index of chronic conditions, self-reported SA, and physical and cognitive function measures were obtained through self-report. These were used to develop a traditional measure of SA based on Rowe and Kahn's definition. However, two different methods were used to define engagement: 1) interpersonal relationships and productive activities; and 2) frequency of participation in 29 activities over a one week period (1-7 days). Using the more traditional definition of engagement 12.3%of participants were determined to be SA, 78.5% moderately SA, and 2.2% not successfully aging. In contrast, using frequency, no participants were identified as not SA while 20.4% were SA and 79.6% moderately SA. Using frequency as a proxy of engagement sends a strong public health message. Participating in a variety of activities at least one time per week on average makes positive contributions to SA, suggesting that even minimal levels of engagement are beneficial.Acknowledgments: Supported by SSHRC (PLW)
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".