Conflict Activity Styles of Psychological Counsellor Candidates: A Study of Based on Forgiveness and Psychological Well-Being
Bibliographic record
Abstract
The purpose of this study is to assess the conflict activity styles of psychological counselor candidates in terms of psychological well-being and forgiveness. The sample of the study consists of a total of 410 individuals, 281 females and 129 males, who are studying at the department of psychological counseling and guidance at 4 different universities located at İstanbul during the 2019-2020 academic year. The study data were collected by “Personal information form”, “Conflict activity styles scale”, “Forgivingness scale” and “Psychological well-being scale”. The data was analyzed with SPSS-21 statistic software program. The first step of the data analysis included the assessment of the relationship between the variables with Pearson correlation analysis, which then followed by hierarchical multiple regression analysis in order to evaluate the psychological well-being and forgivingness as mutual predictors of conflict styles. The obtained results showed that there is a significant correlation between the psychological counselor candidates’ conflict style scores and their psychological well-being and forgivingness scores. Additionally, it was found that these two variables, though in different percentages, are predictor variables of conflict activity styles of psychological counselors. The data were discussed considering the literature to lead variety of suggestions which would serve both the researchers and field practitioners.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".