Goal setting in later life: an international comparison of older adults’ defined goals
Bibliographic record
Abstract
BACKGROUND: Studies of goal setting in later life tend to focus on health-related goal setting, are pre-determined by the researcher (i.e., tick box), and/or are focused on a specific geographical area (i.e., one country). This study sought to understand broader, long-term goals from the perspective of older adults (65 + years) from Australia, New Zealand (NZ), United Kingdom (UK), Ireland, Canada, and the United States of America (USA). METHODS: Through a cross-sectional, online survey (N = 1,551), this exploratory study examined the qualitative goal content of older adults. Thematic analysis was used to analyze the qualitative data, and bivariate analyses were used to compare thematic differences between regions and by participants' sex. RESULTS: Over 60% of the participants reported setting goals, and participants from the Australia-NZ and Canada-USA regions were more likely to set goals than the UK-Ireland region. The following six overarching themes were identified from the 946 goals reported: health and well-being; social connections and engagement; activities and experiences; finance and employment; home and lifestyle; and attitude to life. CONCLUSIONS: This study supports previous research that demonstrates that older adults can and do set personal goals that are wide ranging. These findings support the need for health professionals to consider different methods for elucidating this important information from older adults that builds rapport and focuses on aspects viewed as more important by the older adult and therefore potentially produces improved health outcomes.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".