Examining the unique roles of adaptability and buoyancy in teachers’ work-related outcomes
Bibliographic record
Abstract
We examined two personal resources—adaptability and buoyancy—that are increasingly recognised as important for teachers’ positive work-related outcomes. We sought to extend knowledge about the unique roles of these two personal resources by examining them simultaneously as predictors of teachers’ work-related outcomes. We also examined how two contextual factors (perceived autonomy-support and time pressure) predict these personal resources and, in turn, how the personal resources (and contextual factors) predict three work-related outcomes: organisational commitment, extra-role behaviour, and failure avoidance. With a cross-sectional design and conducted among 264 Australian secondary school teachers (69% female; average age 43 years), this study showed that perceived autonomy-support was positively associated with adaptability and buoyancy. The reverse was true for time pressure, which was negatively associated with adaptability and buoyancy. In turn, adaptability was positively associated with organisational commitment and extra-role behaviour, whereas buoyancy was negatively associated with failure avoidance. Adaptability and buoyancy also acted as mediators in the associations between the contextual factors and the outcomes. Together, findings hold implications for research and practice around supporting teachers in their work.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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 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".