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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".