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Record W2342631012 · doi:10.5539/ies.v9n5p28

Perceived Social Support and Assertiveness as a Predictor of Candidates Psychological Counselors’ Psychological Well-Being

2016· article· en· W2342631012 on OpenAlexvenueno aff
Bünyamin Ateş

Bibliographic record

VenueInternational Education Studies · 2016
Typearticle
Languageen
FieldHealth Professions
TopicProblem Solving Skills Development
Canadian institutionsnot available
Fundersnot available
KeywordsAssertivenessPsychologyScale (ratio)Regression analysisSocial supportClinical psychologyPsychological well-beingSocial psychologyStepwise regression

Abstract

fetched live from OpenAlex

In this research, to what extent the variables of perceived social support (family, friends and special people) and assertiveness predicted the psychological well-being levels of candidate psychological counselors. The research group of this study included totally randomly selected 308 candidate psychological counselors including 174 females (56.5%) and 134 males (43.5%) studying at Erzincan University, Faculty of Education, Psychological Counseling and Guidance Department in 2015-2016 academic year. The age average of the research group was 20.84. Psychological Well-Being Scale, Voltan-Acar Assertiveness Scale, Multidimensional Perceived Social Support Scale, and Personal Information Form were used as the data collection tools in the research. The data obtained in the research were analyzed with stepwise regression analysis method as one of the multiple linear regression analyses methods. According to the research findings, the variables of assertiveness and social support perceived from family, friends and special people significantly predicted psychological well-being.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.078
GPT teacher head0.500
Teacher spread0.422 · 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 source (direct Gemma or distilled Codex), 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

Citations18
Published2016
Admission routes1
Has abstractyes

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