Embracing life's challenges: Developing a tool for assessing resilient mindset in second wave positive Psychology
Bibliographic record
Abstract
The purpose of this study is to develop and validate the Resilient Mindset Scale (RMS), a brief tool designed to measure resilient mindset among Turkish individuals. Additionally, the study aims to explore the relationship between resilient mindset and mental well-being among adolescents and young adults, providing further evidence in this domain. The exploratory factor analysis, conducted with a sample of 327 participants, revealed that the RMS has a unidimensional structure consisting of six items that effectively measure core indicators of a resilient mindset. Subsequent confirmatory factor analysis, conducted with a sample of 338 participants, confirmed the one-factor structure, demonstrating a good-data model fit with strong factor loadings and internal reliability estimates. Further analyses demonstrated moderate to strong correlations between resilient mindset and mental well-being indicators. Moreover, the latent variables path model revealed that the measurement model had a moderate to strong predictive effect on positive academic functioning, psychological well-being, and psychological distress. These findings establish the psychometric reliability and validity of the RMS as a measurement tool for assessing a resilient mindset among adolescents and young adults. Mental health providers can integrate the concept of a resilient mindset into therapeutic approaches and interventions to foster resilience and enhance mental 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".