Study on the Influence of Adult Attachment on College Students' Mental Health and Well-Being
Bibliographic record
Abstract
Objective To explore the impact of adult attachment on college students' mental health and well-being. Methods The “Revised Adult Attachment Scale” (AAS), “Symptom Self-Rating Scale” (SCL-90) and “General Well-being Scale” (GWB) were selected to conduct a questionnaire survey of 200 universities in an undergraduate college in Hengyang. , And perform statistical analysis on the data. Results (1) There was a certain difference in attachment among college students of different genders and professional categories (P<0.05); (2) There were certain differences in the SCL-90 scores of college students of different grades and professional categories (P<0.05); (3) The totality of college students of different genders There is a certain difference in well-being (P<0.05); (4) There are certain positive and negative correlations between adult attachment and SCL-90 score and overall well-being. (5) In the regression analysis of adult attachment to SCL-90, the higher the anxiety score, the higher the scl-90 scale score, and the worse the mental health; the higher the closeness dependence composite score, the lower the scl-90 scale score. Mental health is good. (6) In the regression analysis of adult attachment to overall well-being, the compound dimension of closeness dependence is a positive predictor, and the anxiety dimension is a negative predictor. Conclusion (1) Both adult attachment and SCL-90 have significant differences in professional categories; adult attachment and college students' overall well-being have significant differences in gender. (2) There is a close relationship between adult attachment and college students' mental health and overall well-being; (3) Adult attachment has a predictive effect on college students' mental health and overall well-being.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".