Providing Inclusive Primary School Education for Children with Special Educational Needs in Wartime Ukraine: Challenges and Current Solutions
Bibliographic record
Abstract
Since 2011 primary school students with special educational needs (SEN) in Ukraine have been allowed to study alongside mainstream students in the inclusive education program established by the Ministry of Education and Science. The Russian invasion of Ukraine in February 2022 challenged the possibility of maintaining inclusive education for children with SEN, which required providers to find new solutions. This paper focuses on the first response of inclusion providers in the Ukrainian primary school education system to the challenges of working in wartime from February to May 2022, using teachers in the city of Kryvyi Rih as a case study. A quasi-experimental study (n=495) involved a group of inclusion providers (n=92) in comparison to mainstream primary school teachers (n=403). The research included: collecting data on the professional qualifications and experience of the teachers; questions on changes in the educational process and the number of students; the Psychological Stress Measure; Oldenburg Burnout Inventory; Brief Resilience Scale; and Miroshnyk Teacher’s Roles Self-Assessment Scale (MiTeRoSA), designed as an online survey. The inclusion providers faced numerous challenges due to the war, namely, (a) the enormous workload of preparing for classes (φ*=8.7, p<.01), the extended non-educational work assignments (φ*=5.5, p<.01), working with students (φ*=2.9, p<.01) and their parents (φ*=3.5, p<.01), (b) volunteering at school, and (c) the changed composition of student groups, i.e., students who left school and fled the area (in 64.1% of responses) and incoming students displaced from combat zones (27.2%). Struggling with stress and burnout (self-reported by 48.91% of inclusion providers), using psychological self-care skills and social resilience capacity through the support of the student's parents and colleagues, primary school teachers invented and implemented seven ways to maintain education for the students with SEN, the kind of which depended on the teacher's professional role structure and available social support.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".