Better understanding the professional and personal factors that influence beginning teacher retention in one Canadian province
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In 2019-2020, approximately 300 beginning teachers agreed to participate in the Alberta Teachers’ Association research study designed to better understand the factors that best support early career satisfaction and growth. With attrition rates as high as 50% in the first five years worldwide, more information is needed as to how to retain teachers. Some contexts have a greater retention rate, but why? Is it due to professional or personal factors, or a combination of both? Using a survey design and a focus group that investigated Early Career Teachers (ECT) perceptions of professional development, mentorship, and school contexts, in addition to personal well-being and resiliency characteristics, results from this study demonstrated that both professional and personal factors are equally influential when retaining early career teachers beyond the first three years. Participants reported not only feeling supported and valued by administration and colleagues, they also rated high on competency and resiliency questions. When asked if they could see themselves being a teacher in 10 years, over 77% selected Strongly Agree and Agree. Therefore, when implemented and supported in unison, adaptive professional and personal factors appear to be a highly predictive combination for improved teacher retention.
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.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it