Contextual factors in early career teaching: A systematic review of international research on teacher induction and mentoring programs
Bibliographic record
Abstract
Early career teachers (ECTs) are situated in a dynamic contextual landscape that both influences their development and practice and dictates professional expectations for instruction and professional learning. This systematic review of international research literature sought to establish the understanding of teacher induction and mentoring program support of ECTs through the following research questions: 1) which nations and regions are represented in research literature that details formal or programmatic support of ECTs? 2) what international research evidence is there to describe various contextual factors that affect experiences of ECTs? and, 3) how do teacher induction and mentorship programs respond to the various contextual factors affecting ECTs? Upon detailing our review method and sampling procedures, we synthesize the convergences and divergences of the findings within each of the contextual factors. The conceptualization of contextual factors in this review included social, political, cultural, organizational, and personal forces that influence the professional practices of ECTs. Finally, we summarize the review findings in a heuristic model that offers a visual representation of the implications of our findings, and discuss the implications for policy, practice, and future research.
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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 | 0.012 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".