Learning together for effective collaboration in school-based occupational therapy practice
Bibliographic record
Abstract
BACKGROUND: School-based occupational therapy (SBOT) practice takes place within a complex system that includes service recipients, service providers, and program decision makers across health and education sectors. Despite the promotion of collaborative consultation at a policy level, there is little practical guidance about how to coordinate multi-agency service and interprofessional collaboration among these stakeholders. PURPOSE: This paper reports on a process used to engage program administrators in an examination of SBOT collaborative consultation practice in one region of Ontario to provide an evidence-informed foundation for decision making about implementation of these services. METHODS: Within an appreciative inquiry framework (Cooperrider, Whitney, & Stavros, 2008), Developmental Work Research methods (Engeström, 2000) were used to facilitate shared learning for improved SBOT collaborative consultation. Program administrators participated alongside program providers and service recipients in a series of facilitated workshops to develop principles that will guide future planning and decision making about the delivery of SBOT services. FINDINGS: Facilitated discussion among stakeholders led to the articulation of 12 principles for effective collaborative practice. Program administrators used their shared understanding to propose a new model for delivering SBOT services. IMPLICATIONS: Horizontal and vertical learning across agency and professional boundaries led to the development of powerful solutions for program improvement.
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 | 0.023 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.023 | 0.021 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".