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Record W7117762708 · doi:10.47678/cjhe.v55i4.190519

From Compliance to Co-Design: Transforming Accommodation Services at a Canadian College Through a People-Centred Service Design and Change Management Approach

2025· article· en· W7117762708 on OpenAlexaffvenueabout
Sterling Crowe, Anna Ghoneim

Bibliographic record

VenueCanadian Journal of Higher Education · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicHealthcare innovation and challenges
Canadian institutionsHumber Polytechnic
Fundersnot available
KeywordsScholarshipAccommodationLeverage (statistics)Psychological interventionChange management (ITSM)Organizational changeCompliance (psychology)InstitutionService (business)

Abstract

fetched live from OpenAlex

This study explores the transformation of academic accommodation services at a Canadian post-secondary institution through the integration of service design, systems thinking, and critical disability theory in the people-centred systems change (PCSC) framework. Using a mixed-methods approach combining interviews, operational data, and co-design sprints the research examined how outdated, compliance-driven models can evolve into values-based, learner-centred systems. Key interventions included a tiered intake process, self-directed renewal options, and faculty development modules, resulting in reduced wait times, increased user satisfaction, and improved operational efficiency. Findings highlight how student experience can serve as a leverage point for systemic change and demonstrate that meaningful redesign is possible even within complex institutional environments. This case contributes to growing scholarship on equity-informed service innovation in higher education.

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 imitation

Not 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.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.906
Threshold uncertainty score0.681

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0190.020
Scholarly communication0.0120.004
Open science0.0040.011
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.170
GPT teacher head0.379
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes3
Has abstractyes

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