Why Attachment Matters: First-Year Post-secondary Students’ Experience of Burnout, Disengagement, and Drop-Out
Bibliographic record
Abstract
Despite considerable evidence that attachment theory is a valuable framework for understanding educational outcomes, associations between attachment representations, academic burnout, engagement, and drop-out have been largely overlooked. In this study, 290 first-year post-secondary students completed attachment, academic burnout, and academic engagement questionnaires; 15% of the 290 students did not return for their second year. Using Structural Equation Modelling, we were able to simultaneously test the associations among variables while controlling for measurement error which may attenuate or overestimate the associations between variables. We also tested whether the associations were similar when the decision to drop-out was added to the model. Attachment anxiety, but not attachment approach-avoidance, was found to be associated with higher burnout and lower engagement. Furthermore, higher burnout increased chances of drop-out. Implications of these findings for universities include consideration of attachment relationships when developing interventions to reduce student burnout, disengagement, and drop-out is discussed.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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 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".