A study of Iranian EFL teachers’ perspective about the impact of caring for teachers on nurturing caring teachers
Bibliographic record
Abstract
While research in second language acquisition (SLA) has accentuated the importance of positive teacher-student relationships, little is known about how teachers themselves experience care in their workplaces and how this affects their teaching practices. Addressing this underexplored gap, the current study investigates the perspectives of English as a Foreign Language (EFL) teachers on the care they receive from institutions, colleagues, and students, and how it shapes their ability to provide care to learners. To this end, written narratives from 30 private institute EFL teachers were thematically analyzed using MAXQDA. The findings unveiled many teachers believed working in a/an (un)caring environment impacted their emotional and interpersonal abilities. They characterized caring work settings with receiving support from educational stakeholders, recognition of teachers’ efforts, and good treatment by one’s students. Conversely, they related uncaring work environments to low payment, dissatisfaction with managers’ behavior, and students’ uncaring behaviors. Teachers acknowledged that a nurturing workplace enhanced their emotional health and their job-related satisfaction, performance, and relationships. To develop such an environment, they recommended actions such as creating supportive networks among teachers, advocating students’ supportive and respectful classroom conduct, and creating policies that prioritize teacher wellbeing. Findings suggest receiving collegial and institutional care could develop teachers’ ability to create a nurturing classroom social climate and build rapport with students. Ultimately, the study emphasizes teachers’ capacity to care is deeply influenced by the appreciation and support they receive, underscoring the need for systemic and institutionalized approaches to teacher care in EFL contexts.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".