An Inquiry into the Mental Health and Well-Being of Early Childhood Educators Who Have Worked within the Contemporary Ontarian Early Learning and Child Care Context
Bibliographic record
Abstract
In Ontario, well-being is considered a foundation for learning and living well alongside young children (Ontario Ministry of Education, 2014), yet, pursuing well-being of early childhood educators (ECEs) is under-valued and under-studied. Previous literature has demonstrated inadequate working conditions and compensation for ECEs (e.g., Flanagan et al., 2013; Langford et al., 2020), who experience poor well-being, globally (Blöchliger & Bauer, 2018; Wells, 2015; Whitaker et al., 2013), yet adequate well-being in Quebec (Royer & Moreau, 2016). This study is the first inquiry into ECE’s well-being in Ontario and utilized thematic analysis to analyze the data from 15 interview transcripts. The results suggest that well-being reflects the relational work within a stressful sector, with little acknowledgement for the intensity, autonomy over work, and access to meaningful support. Unexpectedly, educators described their well-being as a relational negotiation between self (agency, identity), and other (connection, relational stress, alignment). Supporting the everyday life challenges and significantly adverse events of ECEs remains a pursuit for future research (Corr et al., 2017).
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 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".