A Non-bipartite Propensity Score Analysis of the Effects of Teacher–Student Relationships on Adolescent Problem and Prosocial Behavior
Bibliographic record
Abstract
Previous research suggests a link between the quality of teacher-student relationships and the students' behavioral outcomes; however, the observational nature of past studies makes it difficult to attribute a causal role to the quality of these relationships. In the current study, therefore, we used a propensity score analysis approach to evaluate whether students who were matched on their propensity to experience a given level of relationship quality but differed on their actual relationship quality diverged on their concurrent and subsequent problem and prosocial behavior. Student/self, teacher, and parent- (only waves 1-3) reported data from 8 waves of the Zurich Project on the Social Development of Children and Youths (z-proso), a longitudinal study of Swiss youth among a culturally diverse sample of 7- to 15-year-olds were utilized. The initial sample included 1483 (49.4 % female) students for whom information relevant for this study was available. The sample represented families from around 80 different countries, from across all the continents; with approximately 42 % of the female primary caregivers having been born in Switzerland. Following successful matching, we found that students who reported better relationships with their teachers and whose teachers reported better relationships with them evidenced fewer problem behaviors concurrently and up to 4 years later. There was also evidence for an analogous effect in predicting prosocial behavior. The implications of these findings are discussed in relation to prevention and intervention practices.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".