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Record W1894215461

Üniversite Öğrencilerinin Yaşadıkları Problemler ve Psikolojik Yardım Arama Gönüllükleri

2012· article· tr· W1894215461 on OpenAlexaff
Serdar Erkan, Yaşar Özbay, Zeynep Cihangir Çankaya, Şerife Terzi

Bibliographic record

VenueEĞİTİM VE BİLİM · 2012
Typearticle
Languagetr
FieldHealth Professions
TopicProblem Solving Skills Development
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsTurkishPsychologyTest (biology)Scale (ratio)YardSocial psychologySample (material)Geography
DOInot available

Abstract

fetched live from OpenAlex

This study aimed to explore the relationships between Turkish university students’ problem areas, problem levels and their preferences and willingness for help. The sample of the study included 5829 undergraduate students (2974 females, 2841 males, 14 unknown). In order to collect the data, Problem Concern Scale, Psychological Help Seeking Willingness Scale and Information Sheet were used. To examine the differences in students’ scores, t-test, one way analyses variance, and Bonferroni Multiple Comparison Test were administered. The results of analyses showed that there were significant differences between female and male students in problem areas and psychological help seeking willingness. Students from higher socio-economic status have more problems than others. Turkish university students experienced mostly emotional, academic and economic problems. Also, students were more willing to seek help from their family and friends. In general, students’willingness to seek help from professionals was at moderate level.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.342
Teacher spread0.287 · 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

Citations31
Published2012
Admission routes1
Has abstractyes

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