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Record W3057291816 · doi:10.5430/wje.v10n4p35

Conflict Activity Styles of Psychological Counsellor Candidates: A Study of Based on Forgiveness and Psychological Well-Being

2020· article· en· W3057291816 on OpenAlexvenueno aff
Durmuş Ümmet

Bibliographic record

VenueWorld Journal of Education · 2020
Typearticle
Languageen
FieldPsychology
TopicForgiveness and Related Behaviors
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyForgivenessStatisticScale (ratio)Regression analysisSocial psychologyMultilevel modelSample (material)Clinical psychologyApplied psychologyStatistics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.214
Threshold uncertainty score0.546

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.055
GPT teacher head0.387
Teacher spread0.332 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2020
Admission routes1
Has abstractyes

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