The impact of support on growth in teacher-efficacy: a cross-cultural study
Bibliographic record
Abstract
Purpose Perceived support from co-workers and managers is important for many organizational outcomes. However, the benefit of competence support from colleagues and school management on personal teacher efficacy has not been investigated. The purpose of this paper is twofold: first, to investigate the impact of competence support from colleagues and the school management on growth in teacher efficacy and second, to investigate cultural differences (Canada and Sweden). Design/methodology/approach The authors administered an inventory measuring support for competence and personal teacher efficacy to over 400 teachers in Canada and Sweden at 27 schools, at two times. Time 1 took place at the first week of a fall semester and Time 2 at the end of the same semester. Findings Structural equation modeling revealed that competence support from colleagues predicted growth in teacher efficacy, whereas competence support from school management did not. No differences in these relations emerged between Canadian and Swedish teachers. Practical implications The findings have implications for how schools organize teachers in teacher teams so that competence support from co-workers is promoted. Originality/value This study is the first cross-cultural study to empirically show that teachers’ self-efficacy is significantly benefitted by competence support from their teacher peers.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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 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".