Health professional–educator collaboration in the delivery of school‐based tiered support services: A qualitative case study
Bibliographic record
Abstract
BACKGROUND: Educators and health professionals support the learning and participation of diverse children in school environments. Tiered approaches to service delivery may assist these efforts through consideration of universal supports that are useful to all children, targeted supports for some children and individualized supports for the smallest number of children. This study explored how an interprofessional team worked with educators to develop and implement tiered services in two school communities where many families experience economic and social disadvantages. METHODS: Using a participatory action research approach and qualitative case study methods, the research and stakeholder teams jointly designed and conducted this study in two schools during the 2017-2018 school year. Data collected included weekly logs written by the interprofessional team members and 16 interviews conducted with team members, parents, educators and administrators. RESULTS: The team provided a variety of services to individual students, groups, whole classes and the school community. Collaboration and communication were needed to define roles and expectations and to plan and share student information. Reported benefits included timely service, capacity building and student goal achievement. The main barriers were related to service fragmentation, time and workload. CONCLUSIONS: Recommendations included clearer direction about expectations and improved coordination within the systems that offer services. Further research should include exploration of comparative cases with varying contexts, the inclusion of child perspectives and direct observation.
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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 | 0.024 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| 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".