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Record W3009538910 · doi:10.3390/ijerph17051561

Examining the Impact of Knowledge Mobilization Strategies to Inform Urban Stakeholders on Accessibility: A Mixed-Methods study

2020· article· en· W3009538910 on OpenAlexafffund
Delphine Labbé, Atiya Mahmood, William C. Miller, W. Ben Mortenson

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsInternational Collaboration On Repair DiscoveriesSimon Fraser UniversityGF Strong Rehabilitation CentreUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsStakeholderCitizen journalismStakeholder engagementPerspective (graphical)Urban planningEnvironmental planningBusinessKnowledge managementPublic relationsComputer sciencePolitical scienceGeographyEngineering

Abstract

fetched live from OpenAlex

Urban areas offer many opportunities for people with disabilities, but limited accessibility may prevent their full engagement in society. It has been recommended that the experience-based perspective of people with disabilities should be an integral part of the discussion on urban accessibility, complementing other stakeholder expertise to facilitate the design of more inclusive environments. The goals of this mixed-method study were to develop knowledge mobilization (KM) strategies to share experience-based findings on accessibility and evaluate their impact for various urban stakeholders. Using a participatory approach, various KM strategies were developed including videos, a photo exhibit and an interactive game. These strategies were evaluated based on various impact indicators such as reach, usefulness, partnerships and practice changes, using quantitative and qualitative methods. The findings suggested that the KM strategies were effective in raising the awareness of various urban stakeholders and providing information and guidance to urban planning practices related to accessibility.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.614
Threshold uncertainty score0.246

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.303
GPT teacher head0.465
Teacher spread0.162 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations24
Published2020
Admission routes2
Has abstractyes

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