How prepared are people for their future? Findings from the <i>Preparedness for the Future</i> survey
Bibliographic record
Abstract
Objective: People are living longer than ever before. Many arrive at a later stage of life in poor health and with inadequate financial and social resources. The purpose of this paper is to describe people’s general state of preparedness for their future as older persons, identify specific attitudes towards ageing and key characteristics that portend a lesser degree of preparedness, and identify the issues that need greater emphasis. Design: Cross-sectional survey. Setting: 502 adult participants enlisted on an online polling panel in Canada. Methods: Demographics, attitudes towards the future self and ageing and the responses to the ‘Preparedness for the Future Questionnaire’ (Prep-FQ) were analysed. Descriptive statistics were used to highlight overall and domain scores (possible score 0–100). Regression models were used to link key demographic characteristics and attitudes to a lower Prep FQ score. Results: The average age of participants was 54.1 years old (range 30–91). The majority (97%) felt it was important to think about themselves as an older person, yet less than 25% of people regularly spent time thinking about what it would be like for them as an older person. The average score on the Prep FQ was 61.6 (range 25–99). Items with the lowest scores were related to advance serious illness planning (medical care, funeral and legacy planning). Factors associated with a higher Prep FQ score included being female, having more education, thinking about when they are older and feeling positive about themselves as an older person. Conclusion: Helping people think and plan ahead more for healthy ageing may help some people move forward with confidence in creating a long, high-quality life and high-quality death. Helping ‘at-risk’ people plan for serious illness in advance is a high-priority target for improving people’s general state of preparedness for the future.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".