Fear as a Predictor of Life Satisfaction in Retirement in Canada
Bibliographic record
Abstract
In developed countries, healthy retirees can fulfill their life, but may fear growing old. Yet, there is little empirical data on the relationship between this fear and life satisfaction. This cross-sectional, correlational survey study tested whether a new, summated measure of Fears About Growing Old (FAGO)—derived from exemplifications of Laslett, who posited the theory of the Third Age—significantly predicted life satisfaction and retirement satisfaction after adjusting for significant social participation covariates. A total of 190 Canadian retirees at three senior centers in Ontario, Canada, completed surveys. A pilot study established the reliability and validity of the scales, including the FAGO, used to assess the independent variable. In a regression analysis, fear (R 2 change = .06) was found to be a statistically significant predictor of life satisfaction when controlling for five covariates (current activity, circumstance and pursuing own interest as two reasons for retirement, postretirement work, and perceived social support); overall R 2 = .26. For retirement satisfaction, fear significantly explained variance in the outcome (R 2 change = .04) while controlling for two significant covariates (current activity and perceived social support); overall R 2 = .14. A work by gender interaction on satisfaction was not found. Other than fear about loss of mobility, men rated loss of partner very high; women rated mortal disease very high. The lowest fear was loss of retirement income for men and loss of earning-power for women. Canada's poverty preventive programs successfully supported senior postretirement life. The FAGO was useful to find senior needs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".