Developing Resilience and Promoting Well-being in Early Career Teaching:
Bibliographic record
Abstract
Our multi-phase pan-Canadian research study examined the differential impact of teacher induction and mentorship programs on the retention of early-career teachers (ECTs). One of the research phases—interviews—explored the lived experiences of novice professionals during their first years of teaching as they dealt with requirements, expectations, and challenges. In this article, we describe the perceptions of the ECTs (N = 36) regarding their needs, hopes, and concerns in relation to developing resilience and promoting well-being for ECTs across Canada. Based on the phenomenological analysis of the data, four themes emerged: cultivatinga work-life balance; nurturing a positive mindset; committing to reflective practices; and consulting, connecting, and collaborating with others. These ECTs, who sometimes thrived, and other times struggled, were able to articulate and contextualize their experiences and actions within high-demand environments of early career teaching, and provided useful insights for other ECTs’ resilience and well-being. This article concludes with implications for research, practice, and school leadership in the areas of teacher induction and mentoring.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".