Teachers in the Trenches: Exploring Canadian-Certified Early-Career Teachers' Experiences of Turnover and Retention in International Schools in China
Bibliographic record
Abstract
Teacher turnover, often referred to as teacher attrition or migration, has been a growing worldwide concern for many years, particularly for teachers within their first five years of the teaching profession. Multiple studies have been conducted that identify the causes of teacher turnover and the possible teacher retention strategies within schools that can reduce the impact of this problem. Despite having knowledge of the factors that cause teacher turnover and the potential solutions for teacher retention, much of the research available on teacher turnover is both US-based and quantitative in nature, and as a result the unique and descriptive accounts of the human voices that experience the issue are often underrepresented from outside North America. \nInspired by my own experiences while working in an international school, this phenomenological study was conducted with the purpose of discovering and exploring the unique experiences of Canadian-certified early-career teachers surrounding the challenges, barriers and supports connected to teacher turnover and retention decisions in international secondary schools that use a Canadian curriculum in China. In order to carry out this study, a combination of a survey and individual interviews was used. Surveys were analyzed descriptively while interview data were audio recorded, transcribed verbatim and analyzed using a general inductive approach. \nThe findings of this study suggest that turnover and retention decisions in China are highly individualistic in nature and depend on a multitude of different contextual and individual factors. However, five main themes emerged from participants’ accounts which were influential in turnover and retention decisions. They included: participants motivations for working in China; barriers that influence turnover decisions; existing supports to overcome turnover challenges; supports teachers feel would be beneficial to enhance retention decisions; and, advice from teachers to teachers. By exploring these themes, a more comprehensive understanding of beginning teachers’ perceptions and experiences surrounding the phenomenon of turnover and retention decisions in international schools in China emerged. Moreover, the importance of supporting early-career teachers both individually and professionally during the transition period into the teaching profession was highlighted throughout this study.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".