Well-being balance and lived experiences: understanding the impact of life situations on human flourishing
Bibliographic record
Abstract
Background This study aimed to determine the most significant indicators of positive well-being and understand differences in sources of well-being across different life situations, age groups, genders, and income levels, utilizing a novel measure of positive well-being, the Well-being Balance and Lived Experiences (WBAL) Assessment, which evaluates the frequency of various positive experiences and feelings across a range of activation and arousal levels that have previously been demonstrated to affect subjective well-being and human flourishing. Methods A sample of 496 evaluable subjects aged 20-69 and census-balanced for gender were recruited from a U.S. population panel. Differences in well-being and sources of well-being were analyzed across subgroups via MANOVA analysis followed by post-hoc ANOVA and Tukey’s HSD analyses using Cohen’s d to determine size and direction of effects between categorical subgroups. Results Life situations, including relationship, parenting and employment status, were shown to have a more significant effect on overall well-being than the demographic variables of age, gender and household income. Reported well-being improved significantly with life situations, including companionate relationships ( d =0.38, p <0.001) and parenting ( d =0.35, p <0.001), that provide greater opportunities for more frequent social connection ( d ’s=0.25, p <0.01 to 0.62, p <0.001) and purposeful contribution to others’ well-being ( d ’s=0.34 to 0.71, p <0.001), associated with increased feelings of significance ( d ’s=0.40 to 0.45, p <0.001) and efficacy ( d ’s=0.37 to 0.44, p <0.001). An age-related positivity effect was observed, with older adults reporting more frequent positive feelings than younger age groups ( d =0.31, p <0.01). Measures of mindset positivity, variety of positive experiences and feelings, and frequency and range of positive feelings across arousal levels each corresponded closely with overall well-being. Conclusion Life situations, including relationship, parenting and employment status, had a more broad and significant effect on wellbeing than age, gender or income. Across life situations, purposeful contribution and social connection, with associated feelings of efficacy and significance were key drivers of differences in well-being. Mindset positivity and variety of positive experiences and feelings correspond closely with overall well-being. Findings from this study can help guide the design and implementation of intervention programs to improve well-being for individuals and targeted subgroups, demonstrating the utility of the WBAL Assessment to evaluate discrete modifiable sources of positive well-being.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".