Early childhood professional well-being in Nova Scotia: A mixed methods study
Bibliographic record
Abstract
Ensuring access to high-quality programs and services can improve children’s long-term health and education outcomes and reduce inequities in health, income, and education in the population. The professional well-being of those working in the early childhood education sector has been negatively associated with levels of turnover, which in turn can impact program quality more broadly. Considering the variability of professionals working in the field of early childhood, it is important to better understand how their experiences influence their professional well-being. This study aims to address this gap, through a mixed methods exploration of survey responses to items on an Early Childhood Professional Well-Being questionnaire. We conducted both an Latent Profile Analysis (LPA) on 27 items and also a conventional content analysis of one open-response item and then compared and contrasted results to create a fulsome illustration of patterns of early childhood professional well-being. The LPA revealed five distinct profiles of professional well-being: highest well-being, generally high well-being, active agency, comfortable well-being, and low wellbeing. Years of experience and newcomer status had a relationship to profile membership, with more years of experience associated with higher professional well-being and newcomer status with lower or medium levels of well-being. Our content analysis revealed mostly negative perspectives regarding professional well-being, most often due to various, overlapping external factors. Greater understanding of profile membership and what may impact it as well as the larger external systems that affect early childhood professionals will help to inform policy supports for professional well-being of specific individuals, thereby promoting professional wellbeing and, in turn, overall program quality.
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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 | 0.023 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 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".