Comparison of happiness and willingness to communicate in attachment styles in university students
Bibliographic record
Abstract
Introduction: The aim of this study was to compare happiness and willingness to communicate in attachment styles in sample students in University of Tabriz. Method: Using cluster sampling, 400 students were selected and for data collection, Hazan and Shaver Attachment Styles Scale and Memorial University of Newfoundland Scale of Happiness (Munsh) and Willingness to Communicate Scale were used. Data analysis using one-way analysis of variance and the post hoc multiple comparisons were performed. Results: The results showed that there are significant differences between attachment styles and happiness. Students with secure attachment style had a higher happiness than non-secure students with avoidant and ambivalent attachment styles. Students with avoidant attachment style had higher happiness than those with ambivalent attachment style. Another result showed that there are significant differences between attachment styles and willingness to communicate. Conclusion: Students with secure attachment style had higher willingness to communicate than non-secure students and students with ambivalent attachment style had higher willingness to communicate compared with avoidant students. Overall, attachment styles affect happiness and willingness to communicate.
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 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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".