Sources of Life Strengths as Predictors of Late-Life Mortality and Survivorship
Bibliographic record
Abstract
The aim of the research was to determine within a single study the extent to which demographic factors, self-rated-health and psychosocial factors present the strongest risks or benefits to older adults' mortality in the course of a 5.9-year longitudinal follow-up. The initial sample of 732 individuals was drawn randomly from the registry listings of four municipal branch offices of the Social Services and Community Associations for seniors in Southern Alberta. The final recruitment of 380 participants was based on a representative sample of elders who volunteered participation. A three-part Cox regression analyses model of predictor variables, controlling for age and subsequently controlling for self-rated health and self-rated physical functioning, was implemented to study gender differences in a number of socio-demographic and psychosocial factors, including individuals' sources of internal strengths. As hypothesized, individuals' sources of internal strengths (i.e., Perceptions of Self -Efficacy, Internal Control, Personal Maturity, Personal Commitments, and Social Engagement) are central to the prediction of mortality of both men and women. For men lower education and low levels of perceived internal control, personal commitment, and physical functioning are associated with the greatest threat to mortality but these factors are largely inconsequential for women's survival. By contrast, low levels of perceived social support and social engagement present the greatest risk to women's mortality. Implications of the findings are discussed with respect to factors that contribute to late-life longevity.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".