Gender, Age and Subjective Well-Being: Towards Personalized Persuasive Health Interventions
Bibliographic record
Abstract
(1) Background: Subjective well-being (SWB) is an individual’s judgment about their overall well-being. Research has shown that high subjective well-being contributes to overall health. SWB consists of both Affective and Cognitive dimensions. Existing studies on SWB are limited in two major ways: first, they focused mainly on the Affective dimension. Second, most existing studies are focused on individuals from the Western and Asian nations; (2) Methods: To resolve these weaknesses and contribute to research on personalizing persuasive health interventions to promote SWB, we conducted a large-scale study of 732 participants from Nigeria to investigate what factors affect their SWB using both the Affective and Cognitive dimensions and how distinct SWB components relates to different gender and age group. We employed the Structural Equation Model (SEM) and Confirmatory Factor Analysis (CFA) to develop models showing how gender and age relate to the distinct components of SWB; (3) Results: Our study reveals significant differences between gender and age groups. Males are more associated with social well-being and satisfaction with life components while females are more associated with emotional well-being. As regards age, younger adults (under 24) are more associated with social well-being and happiness while older adults (over 65) are more associated with psychological well-being, emotional well-being, and satisfaction with life. (4) Conclusions: The results could inform designers of the appropriate SWB components to target when personalizing persuasive health interventions to promote overall well-being for people belonging to various gender and age groups. We offer design guidelines for tailoring persuasive intervention to increase SWB based on an individual’s age and gender group. Finally, we map SWB components to possible persuasive technology design strategies that can be employed to implement them in persuasive interventions design.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".