Maturity and Well-Being: Consistent Associations Across Samples and Measures
Bibliographic record
Abstract
Introduction: Researchers have noted an association between maturity and well-being. However, this body of research uses different measures and conceptualizations of maturity (e.g., ego development, psychosocial maturity) and often only a few indicators of well-being. In the present research, we examined associations between a single self-rated measure of maturity and a variety of different indicators of well-being. Furthermore, we examined this association across a variety of samples. We hypothesized that maturity will show a positive relationship with measures related to well-being. Methods: Samples of college students (Studies 1, 3, 4), Star Wars fans (Study 2), and individuals in the U.S., Canada, Brazil, Vietnam, and India (Study 5) completed a short measure of maturity and measures related to well-being. Results: Across the studies, self-rated maturity was consistently positively correlated with various indicators of well-being (e.g., psychological, physical) and related constructs (e.g., self-compassion, empathy). Conclusion: The results highlight the association between maturity and well-being. Furthermore, the results address the fragmented nature of this association in the literature by showing consistent relationships with a variety of well-being indicators with a single measure of maturity. Assessments of maturity may be beneficial in hiring decisions and student evaluation in the healthcare profession.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".