The six “P” model: principles of coaching for inclusion coaches
Bibliographic record
Abstract
Purpose The purpose of this paper is to identify a set of principles that are necessary to overcome the challenges that inclusion coaches encounter with teachers as they transition into an inclusive service delivery model. Design/methodology/approach Online written reflections of 13 inclusion coaches (K-12) who were a part of a larger, mixed-methods research design are the primary data source. For the two years of the project, the inclusion coaches provided bi-annual reflections, each with 7-11 entries. The reflections were downloaded, coded, collapsed, and thematically presented as the inclusion coaches’ perspectives for supporting teachers’ inclusive classroom practices. Findings The findings are presented as six principles for the process of coaching teachers for inclusion: pre-requisite: teachers’ receptivity; process: from building trust to collaborating and reflecting; precipice: tension between knowledge and beliefs; promotion: administrative support; proof: evidence of change, impact, and capacity building; and promise: future of the role. Practical implications These six principles of coaching for inclusion offer considerations, conditions, and guides for inclusion coaches that are striving for fully inclusive classrooms in their jurisdictions. With a view to future practice, the six principles are reiterative as they should be revisited each time a coaching interaction is initiated in a school site and with a classroom teacher. Originality/value As a conclusion, a conceptual model is offered. This spiraling staircase displays the conditions that exist prior to coaching and during coaching interactions and considerations for coaching sustainability.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.050 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".