School climate, school identification and student outcomes: A longitudinal investigation of student well‐being
Bibliographic record
Abstract
BACKGROUND: Schools are increasingly recognized as key facilitators of child and youth well-being. Much attention has been directed to the school social environment and the areas of school climate or school connectedness/identification. Drawing on the social identity approach and related work, it has been argued that school social identification may be the mechanism or process through which school climate comes to impact individual student functioning (Applied Psychology, 28, 2009, 171). Much of the previous research on social identity and well-being, though, is limited because it is cross-sectional. AIMS, SAMPLE & METHODS: This current study aims to advance understanding of the relationships between school climate, school identification and positive and negative well-being. It adopts a three-wave longitudinal sample of Australian students (N = 6537 wave 3, grades 7-10) and incorporates a range of control variables. Multilevel modelling (MLM) is used to test relationships of interest. RESULTS AND CONCLUSIONS: In line with predictions, school identification was a significant mediator of the relationship between school climate and the well-being dimensions of positive affect and depression (but not anxiety). The substantial theoretical and practical implications of this research are discussed, including the role of the school social environment in helping young people successfully transition to adulthood.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".