Associations of Peer and Student-Teacher Relations to Well-Being in Middle School Students
Bibliographic record
Abstract
Early adolescence is a critical period marked by significant social, emotional, and cognitive changes, alongside growing concerns about mental health and well-being, highlighting the need for effective support systems. Supportive student-teacher relationships and peer acceptance are essential for early adolescent development and well-being. However, much of the existing research examines these relationships separately or fails to consider both perspectives simultaneously. The current study examined the alignment between students’ ratings of teacher supportiveness and teachers’ ratings of relationship closeness. It also explored the combined contributions of student-teacher and peer relationships to students’ overall well-being. Data were collected from 252 6th and 7th-grade students from 15 middle school classrooms in a large urban public school district in Western Canada. Measures included students’ reports of their well-being and ratings of supportive student-teacher relationships, teachers’ ratings of close student-teacher relationships, and peer nominations of peer acceptance. Results from multi-level modeling showed that students’ ratings of supportiveness significantly predicted teachers’ ratings of closeness, though this explained a small portion of the variance. Notably, although teachers’ ratings did predict student well-being, students’ ratings of a supportive relationship were the strongest predictor of well-being. Additionally, girls and those identifying outside the binary reporting lower well-being. These results together shed light on the importance of students’ subjective experience and the importance of supportive student-teacher relationships through an ecological, strengths-based lens. Limitations, future directions, and educational applications are discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".