Bibliographic record
Abstract
Background: The issue of the individual's psychological well-being is one of the most interesting and relevant in psychology. Its study has implications not only for theoretical but also for many of the present practical challenges. This study explores predictors of psychological well-being, one of which is resilience. Objective: The purpose of the study is to analyse psychological well-being predictors and prove that resilience is one of its important predictors. Methods: To achieve the aim, a theoretical grounding of the key concepts was made, and an empirical study was carried out. The following techniques were used: the Ryff Scales of Psychological Well-Being, the Freiburg Personality Inventory and the Maddi's Hardiness Survey. Furthermore, at the empirical level, the characteristics of resilience among different age groups were recorded. Results: The study presents the results of a survey involving 150 people of different ages related to the profession of consulting psychologists. Furthermore, the study identified the following predictors of psychological well-being: resilience, femininity/masculinity, positive attitudes, emotional lability, irritability, and aggression. Conclusions: Based on the study of psychological and pedagogical literature and the study findings, it has been concluded that resilience should be considered a mechanism for achieving psychological well-being. The theoretical analysis described the concepts of "psychological well-being" and "resilience", providing their characteristics and factors.
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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 | 0.000 | 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) | 0.006 | 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".