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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".