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Record W4321600344 · doi:10.3389/fresc.2023.1102490

A scoping Review of Tools to Evaluate Existing Playgrounds for Inclusivity of Children with Disabilities

2023· review· en· W4321600344 on OpenAlexaff
Leah G. Taylor, Mara Primucci, Leigh M. Vanderloo, Kelly P. Arbour‐Nicitopoulos, Jennifer Leo, Jason Gilliland, Patricia Tucker

Bibliographic record

VenueFrontiers in Rehabilitation Sciences · 2023
Typereview
Languageen
FieldSocial Sciences
TopicDisability Rights and Representation
Canadian institutionsChildren’s Health Research InstituteUniversity of AlbertaLawson Health Research InstituteUniversity of TorontoWestern University
Fundersnot available
KeywordsCLARITYOperationalizationInclusion (mineral)Grey literatureWhite paperAuditPsychologyPolitical scienceMEDLINEBusinessSocial psychology

Abstract

fetched live from OpenAlex

Introduction: Children with disabilities may be unable engage playground spaces due to barriers exacerbating exclusion. Therefore, clarity on how to evaluate existing playgrounds for inclusivity of children with disabilities is required. Methods: A scoping review was undertaken to explore auditing tools. Results: Fourteen white and grey literature resources were identified. The term "inclusion" was operationalized differently across tools, primarily focusing on physical accessibility. Characteristics of the tools were synthesized into 13 inclusive design recommendations for playgrounds. Two tools showed promise, evaluating 12/13 recommendations. Discussion: The results of this review provide guidance on existing tools for evaluating playgrounds for inclusion for community stakeholders and researchers. Systematic Review Registration: https://osf.io/rycmj.

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)0.063
metaresearch head score (Gemma)0.179
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.063
Threshold uncertainty score0.333

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0630.179
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0060.007
Bibliometrics0.0330.026
Science and technology studies0.0020.003
Scholarly communication0.0060.007
Open science0.0030.006
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.223
GPT teacher head0.499
Teacher spread0.276 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations4
Published2023
Admission routes1
Has abstractyes

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