Inclusion in Quebec childcare centers: financial support, adaptation, and training
Bibliographic record
Abstract
Background Inclusion in childcare centers involves a support system that includes funding policies, inclusive practices and access to ongoing training. The province of Quebec (Canada) benefits from a network of subsidized childcare services for children from birth to 5 years old. Although two financial measures support educational childcare centers welcoming children with disabilities, there is a lack of knowledge about how these measures are used. Research questions The research questions are: (1) How do childcare centers use financial measures to support inclusion? (2) What adaptations are made in childcare centers? and (3) What is the ongoing training of educators to support inclusion? Method This research presents the results of an online survey completed by 94 childcare management staff and 154 childcare educators. The survey was developed using a two-way process involving the researchers along with representatives from government ministries and agencies and childcare management staff. Results Results reveal that just over half of management staff request financial measures for all children with special needs and does so mainly to hire additional and specialized human resources and replace staff in meetings with specialized services. The financial measures are perceived to meet material adaptation needs better than those for human resources. Adaptations made by educators relate to instructions, individual interventions, equipment, group activities, physical environment, and schedule and routines. Over half the educators report that adaptations involving group activities and schedules and routines are the most difficult to implement. Less than half the managers and educators had received ongoing training on inclusive practices. Issues addressed during such training include commonly recognized interventions based on diagnoses, collaboration with parents, collaboration with partner agencies or institutions, instructions, individual interventions, group activities, equipment, physical environment, and schedules and routines. Discussion Results are discussed in terms of (1) needs met and funding model, and (2) dimensions of quality inclusive practices such as leadership of childcare management staff, training and professional development, collaboration with health and specialized social services, learning environments and partnership with families.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".