Feasibility of Evidence-Based Social and Emotional Learning in Prairie Canadian Schools
Bibliographic record
Abstract
This study examined opportunities and barriers for implementing evidence-based Social and Emotional Learning (SEL) programs in Prairie Canadian Schools. Educators from Manitoba, Saskatchewan and Alberta completed an online survey on SEL feasibility and reported on five feasibility domains: (1) attitudes about SEL, (2) knowledge about SEL, (3) job stress, (4) resources for implementing SEL, and (5) SEL practices. Results indicated that positive attitudes toward SEL significantly predicted increased perceived feasibility for evidence-based SEL implementation. Additionally, both knowledge and access to resources predicted increased SEL practice by Prairie Canadian educators. Analyses for open-ended responses paralleled quantitative results. Specifically, Canadian educators had positive views about SEL programing, but like previous research conducted in other countries, indicated that they require better access to SEL training, and resources (e.g., more time to plan and teach SEL, funding and program materials). A unique Canadian context-related finding from this study was that some Prairie Canadian educators indicated a paucity of French materials for SEL programs, which impeded implementation. In order to effectively implement evidence-based SEL in Prairie Canadian schools, policy makers must address the indicated barriers for Canadian educators, such as increased SEL training and resources, and easy access to appropriate French materials.
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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 | 0.029 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".