Creating an inclusive mall environment with the PRECEDE-PROCEED model: a living lab case study
Bibliographic record
Abstract
PURPOSE: Although public environments provide opportunities for participation and social inclusion, they are not always inclusive spaces and may not accommodate the wide diversity of people. The Rehabilitation Living Lab in the Mall is a unique, interdisciplinary, and multi-sectoral research project with an aim to transform a shopping complex in Montreal, Canada, into an inclusive environment optimizing the participation and social inclusion of all people. METHODS: The PRECEDE-PROCEDE Model (PPM), a community-oriented and participatory planning model, was applied as a framework. The PPM is comprised of nine steps divided between planning, implementation, and evaluation. RESULTS: The PPM is well suited as a framework for the development of an inclusive mall. Its ecological approach considers the environment, as well as the social and individual factors relating to mall users' needs and expectations. Transforming a mall to be more inclusive is a complex process involving many stakeholders. The PPM allows the synthesis of several sources of information, as well as the identification and prioritization of key issues to address. The PPM also helps to frame and drive the implementation and evaluate the components of the project. CONCLUSION: This knowledge can help others interested in using the PPM to create similar enabling and inclusive environments world-wide. Implication for rehabilitation While public environments provide opportunities for participation and social inclusion, they are not always inclusive spaces and may not accommodate the wide diversity of people. The PRECEDE PROCEDE Model (PPM) is well suited as a framework for the development, implementation, and evaluation of an inclusive mall. Environmental barriers can negatively impact the rehabilitation process by impeding the restoration and augmentation of function. Removing barriers to social participation and independent living by improving inclusivity in the mall and other environments positively impacts the lives of people with disabilities.
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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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".