With Awareness Comes Competency: The Five Awarenesses of Teaching as a Framework for Understanding Teacher Social-Emotional Competency and Well-being
Bibliographic record
Abstract
Research Findings Developing students’ social and emotional skills positively affects their academic success. The Prosocial Classroom model (PCM) highlights the importance of teacher’s social-emotional competency (SEC) and well-being. The current research extends the PCM by using the Five Awarenesses of Teaching Framework (The Framework) to understand teacher SEC through a developmental lens. We discuss how the Framework can inform and be informed by our understanding of Early Care and Education (ECE) teachers’ SEC. Eighteen ECE teachers participated in cognitive interviews and a deductive thematic analysis was used to categorize teachers’ interview data according to The Framework. Three key themes were identified: 1) The Framework described cognitive capacities relevant to ECE teacher SEC consistent with other teacher populations; 2) there was an underlying conflict between teachers’ keen awareness of student social-emotional learning (SEL) and an active suppression of their own social and emotional well-being; and 3) the impact and importance of race, ethnicity, and family engagement. Practice or Policy: Findings reveal that ECE teachers’ SEC and well-being can be described using The Framework. Additionally, the suppression of ECE teacher emotional needs and the importance of family engagement suggest important avenues for improving our understanding of ECE SEC and well-being.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".