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Record W4415608626 · doi:10.1080/09687599.2025.2570347

Stimulating inclusive outdoor play: breaking the vicious circle between physical segregation and lack of social acceptance

2025· article· en· W4415608626 on OpenAlexaff
Kirsten Visser, Manon Bloemen, Jan Willem Gorter, Karlijn van Ramshorst, Fenne Verhoeven, Rosa de Vries, Sanne Wigmans, Eline A. M. Bolster

Bibliographic record

VenueDisability & Society · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsMcMaster UniversityMcMaster University Medical Centre
FundersNationaal Regieorgaan Praktijkgericht Onderzoek SIA
KeywordsVirtuous circle and vicious circleSocial acceptanceSelf-acceptancePerspective (graphical)

Abstract

fetched live from OpenAlex

Inclusive play is a critical catalyst for social inclusion and has developmental benefits for all children. This research explores barriers that adult stakeholders perceive towards inclusion of children with disabilities in ‘playing together’ with peers with and without disabilities. Insights were gathered through four focus groups (n = 37) involving young adults with a disability, parents of children with and without disabilities, healthcare and welfare professionals, and government organizations. Key barriers identified include the segregation of daily activities of children with and without disabilities, and a lack of social acceptance, both limiting opportunities for inclusive play. Participants proposed a number of solutions, mainly focused on connecting children with and without disabilities, promoting positive attitudes, and optimizing collaboration between different stakeholders. Building local networks of healthcare and welfare professionals, municipal actors, parents, and children is crucial to creating inclusive play opportunities that benefit all children.

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0080.013
Scholarly communication0.0070.005
Open science0.0010.014
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.335
Teacher spread0.313 · 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 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

Citations2
Published2025
Admission routes1
Has abstractyes

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