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Record W4416092337 · doi:10.1080/23748834.2025.2566572

Designing and implementing nature-based interventions in community settings: a scoping review

2025· review· en· W4416092337 on OpenAlexafffund
Shania Bhopa, Laura Banfield, Parsa K. Memon, Adam Watson, Ghanwa Afach, Emma Apatu, Gita Wahi, Diana Sherifali, Sohnia Sansanwal, Sujane Kandasamy, Patricia Montague, Amanda Sim, Andrea Baumann, Sonia S. Anand, Russell J. de Souza

Bibliographic record

VenueCities & Health · 2025
Typereview
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsHamilton Health SciencesImpactMcMaster University Medical CentrePopulation Health Research InstituteMcMaster University
FundersFaculdade de Ciências da Saúde, Universidade de MacauBrock UniversityPublic Health AgencyPublic Health Agency of CanadaMcMaster UniversityCanadian Institutes of Health ResearchNovartisMohawk CollegeCity of Hamilton
KeywordsPsychological interventionWork (physics)Intervention (counseling)Context (archaeology)Qualitative researchSet (abstract data type)

Abstract

fetched live from OpenAlex

Childhood obesity is a pressing global health issue, particularly among children from newcomer families who often face challenges in accessing health-promoting resources. This scoping review, guided by the Arksey and O’Malley framework, investigates the design and implementation of nature-based interventions in community settings to foster healthy active living (HAL) among these children. We screened 4,010 articles, including 50 in this review. Our analysis revealed the theories and frameworks utilized to develop HAL interventions, the way in which nature was utilized by various communities, and how community members are connected to the current available HAL programming. This review underscores the importance of community engagement and addressing existing gaps in nature-based HAL interventions to enhance the health and well-being of children in newcomer families.

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)0.017
metaresearch head score (Gemma)0.054
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 score0.017
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.054
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0090.009
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.087
GPT teacher head0.426
Teacher spread0.339 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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