How Can Peer Group Influence the Behavior of Adolescents: Explanatory Model
Bibliographic record
Abstract
The current work aims to study both the peer group and family influence on adolescent behaviour. In order to achieve the aforementioned objective, an explanatory model based on the Structural Equations Modelling (SEM) was proposed. The sample used was the group of adolescents that participated in the Portuguese survey of the European study Health Behaviour in School-aged Children (HBSC). The Portuguese survey included students from grades 6, 8 and 10 within the public education system, with an average age of 14 years old (SD=1.89). The total sample of the HBSC study carried out in 2006 was 4,877; however with the use of the SEM, 1,238 participants were lost out of the total sample. The results show that peers have a direct influence in adolescents' risk behaviours. The relationship with parents did not demonstrate the expected mediation effect, with the exception of the following elements: relation between type of friends and risk behaviour; and communication with parent and lesser involvement in violence behaviours and increased well-being. The negative influence of the peer group is more connected to the involvement in risk behaviours, whilst the positive influence is more connected with protective behaviours.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
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".