What do Teachers do to Show They Care? Learning From the Voices of Early Adolescents
Bibliographic record
Abstract
This mixed methods study examined how middle school students (ages 11–13) in middle-class neighborhoods in Western Canada characterized a caring teacher. Specifically, qualitative content analysis was conducted on 199 sixth and seventh grade students’ written responses to the question “What are three things that teachers do to show they care?” Guided by recent work in the area of mindfulness in teaching, we identified 19 themes: one was categorized as General Teaching (e.g., “They teach”), and 18 that were grouped into one of three thematic categories associated with mindful teaching: Calm (e.g., calm/not reactive), Clear (e.g., democratic communication), and Kind (e.g., empathy). In their descriptions of caring teachers, almost all students (97.5%) used Kind themes, while many used Clear themes (41%); with fewer students using Calm (13.6%) or General Teaching (10.6%) themes. Chi square analyses revealed that girls were more likely to mention Clear themes compared to boys, while boys were more likely to use the specific themes of nurturance and helpfulness. Additional demographic differences were also explored. This research adds to the growing body of studies aimed at elucidating the role of mindful teaching in caring student-teacher relationships, particularly from the perspectives of early adolescents.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".