The well-being of the early career teacher: a review of the literature on the pivotal role of mentoring
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the extant literature with regard to the role of mentorship in promoting the well-being of early career teachers. Design/methodology/approach This paper was comprised of a review of the current literature. Key terms were used to identify initial sources. The search was narrowed further by using the Boolean operator AND to link key terms. Findings Much of the literature exploring mentorship and induction focuses on the formal structures and the targeted learning outcomes of the processes. However, the emotional and personal support afforded new teachers through the development of relationships with mentors is being recognized as contributing high value to the continued retention efforts. Although there are promising practices with regard to induction programs and formal mentorship arrangements in some schools, these practices are very scattered and may not even be equally well established within one school district. Research limitations/implications Implementation of models that are focused on personal and professional support of new teachers could provide an avenue of research examining teachers’ perceptions of well-being and resiliency. Longitudinal, pan-provincial and pan-national research is necessary for developing more support for systemic implementation of mentorship models. Originality/value While there is research identifying existing programs and induction models, this paper uses the lens of early career teacher well-being to point out promising practices and additional considerations for adopting a holistic approach to mentorship. This mentorship model may result in better personal and professional outcomes for new teachers.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".