Understanding the Needs of Primary School Teachers in Supporting Their Students' Emotion Regulation
Bibliographic record
Abstract
BACKGROUND: Accumulating evidence has underscored the importance of fostering children's emotion regulation (ER) within primary school settings and the role of teachers in such efforts. This study sought to assess the needs of teachers in supporting students' ER, through a better understanding of teachers' perceptions and use of healthy versus unhealthy ER strategies in the classroom. METHODS: Primary school teachers (n = 212; 91% female) completed an online, researcher-developed needs assessment survey assessing their perceptions regarding the importance of ER instruction and challenges surrounding children's ER, as well as the perceived effectiveness and reported use of healthy and unhealthy ER strategies in the classroom. RESULTS: Cochran's Q and chi-square analyses revealed misperceptions regarding the effectiveness of healthy and unhealthy strategies, as well as discrepancies between teachers' perceptions regarding the effectiveness of specific healthy strategies (eg, meditation) and their reported use of them. CONCLUSIONS: While teachers recognize the growing importance of fostering ER in the classroom, the present findings suggest that there is a need for more professional development regarding the effectiveness and implementation of ER strategies in the primary school context. Efforts should be made to provide teachers with concrete recommendations for the implementation of ER strategies in the classroom.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".