Measuring Personal and Social Responsibility: An Existential Positive Psychology Approach
Bibliographic record
Abstract
Responsibility was regarded as essential for wellbeing, and measuring this construct is warranted to develop strategies that promote people’s mental health and well-being. The purpose of the current study is to investigate the initial development and validation of the Responsibility Scale (RS) to measure the sense of responsibility of individuals. Participants included two independent samples, comprising of 284 adults, ranging in age between 18 and 84 years. Sample 1 was used to conducted the exploratory factor analysis and comprised of 152 adults (65% female), ranging in age from 18 to 82 years (M = 43.18, SD = 14.68). Sample 2 was used to conduct the confirmatory factor analysis. The sample consisted of 132 adults (56% female), ranging in age from 18 to 84 years (M = 29.08, SD = 12.45). Findings from exploratory factor analysis revealed the RS provided a two–factor solution comprising of 8 items that accounted for 46% of the variance, with equal items targeting characteristics of both personal and social responsibility. Confirmatory factor analysis confirmed the two–factor latent structure, providing good data-model fit statistics. Further results also showed that the internal reliability of the scale and its subscales were strong. Finally, the latent path model revealed that the first– and high–order measurement model had positive and significant predictive effects on life satisfaction and negative predictive effects on psychological distress, accounting for the approximately large variance in the variables. Overall, the results suggest that the RS could be used to assess personal and social responsibility among adults.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| 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".