Relationship between character strengths, well-being and professional skills among trainees in the field of counselling and psychotherapy
Bibliographic record
Abstract
Positive psychology offers a promising framework for clinical supervision aimed at fostering the development of emotionally competent and available counsellors and psychotherapists. The purpose of this predictive correlational research using a pre–post measure is to explore the role of character strengths and strengths use on supervisee (N = 53) well-being and their skill development for counselling and psychotherapy. Results of regression analysis found that the character strengths of humility and hope were significantly associated with an increase in supervisees’ physical well-being. Furthermore, the virtues of courage and justice negatively predicted counselling dispositions and behaviours. The results highlight the importance of openness to self-awareness and knowledge in the context of clinical training and supervision to promote both trainee well-being and skill development and suggest that the experience of clinical supervision for counsellor and psychotherapy trainees may be facilitated by exercising the strengths of humility and hope.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".