Factors Affecting Aggressiveness among Young Teenage Girls: A Structural Equation Modeling Approach
Bibliographic record
Abstract
Adolescence is a period of transition for developmental and social domains that may also be accompanied by behavioral problems. Aggressive behavior may be a mental health concern for young teens and is defined as a behavioral and emotional trait that may be distressing for others. This study aimed to understand the factors associated with aggressiveness among young teenage girls. A cross-sectional study was conducted among a sample of 707 female middle school-aged students using multistage random sampling in Tabriz, Iran. The variables of interest were aggressiveness, general health status, happiness, social acceptance, and feelings of loneliness. Structural equation modeling was employed to analyze the data. Low parental support, low satisfaction with body image, high sense of loneliness, and lower perceived social acceptance were found to be the factors influencing aggressiveness. The current study found that the school environment, home environment, individual and interpersonal factors all play a part in aggressiveness. As a result, the contributing elements must be considered when creating and executing successful interventions to improve this population's 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 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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".