Use of Evidence-Based Survey Methods to Explore Early Elementary School Teachers’ Approaches to Managing Student Anxiety
Bibliographic record
Abstract
The purpose of this study was to describe and explore early elementary school teachers’ practices with regard to addressing student anxiety, focusing on the types of anxiety-reducing strategies they teach in their classes, the level at which they teach them (i.e., on an individual, small group, or whole class level), and the nature of the approach they use (i.e., proactive or reactive). Using a modified version of Tailored Design Methodology (TDM; Dillman, Smyth, & Christian, 2014), survey results (N=190 teacher participants; 64% response rate) indicated that almost two-thirds (66%) of teachers affirmed that student anxiety was impacting their classrooms. Almost all teachers (i.e., 90%) acknowledged teaching multiple anxiety-reducing strategies to their students, contrary to expectations. Survey participants most commonly reported teaching strategies to the whole class, as opposed to teaching strategies to small groups of students or to students on an individual basis. Use of reactive, as opposed to proactive approaches to teaching these strategies were more often reported. Implications are provided for how school personnel can support teachers in using a more proactive approach and ensuring that targeted instruction is available for students with more intensive needs.
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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 | 0.229 | 0.394 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".