MétaCan
Menu
Back to cohort
Record W4214741876 · doi:10.1080/09575146.2022.2034140

Perceived challenges of early childhood educators in promoting unstructured outdoor play: an ecological systems perspective

2022· article· en· W4214741876 on OpenAlexaffabout
Tina Cheng, Mariana Brussoni, Christina Han, Fritha Munday, Megan Zeni

Bibliographic record

VenueEarly Years Journal of International Research and Development · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsBC Children's HospitalUniversity of British Columbia
Fundersnot available
KeywordsEarly childhoodPerspective (graphical)PsychologyOutdoor educationEcological systems theoryFace (sociological concept)Medical educationPublic relationsPedagogyDevelopmental psychologyMedicinePolitical scienceSociology

Abstract

fetched live from OpenAlex

Unstructured outdoor play has been recognized for its beneficial impacts on children’s healthy development; however, unfortunately, opportunities for children to engage in meaningful play are limited. Early learning and childcare centres can be essential settings for unstructured outdoor play, and educators can play a vital role in supporting children’s opportunities, yet they face numerous barriers. We conducted five focus groups with 40 professionals working in the early childhood education field (educators, students and licensing officers) in British Columbia, Canada, to examine their experiences and perceived challenges in promoting children’s unstructured outdoor play. Participants’ identified challenges were mapped on the ecological system and ranged from microsystem concerns (e.g. knowledge and skills) to mesosystem concerns (e.g. lack of shared understanding with parents and colleagues), exosystem concerns (e.g. licensing regulations) and macrosystem concerns (e.g. societal risk aversion). We recommend evidence-based strategies to address each of the identified barriers, targeting each ecological system level.

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)0.004
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.744
Threshold uncertainty score0.644

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.059
GPT teacher head0.376
Teacher spread0.317 · 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 designQualitative
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

Citations36
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueEarly Years Journal of International Research and DevelopmentSame topicEarly Childhood Education and DevelopmentFrench-language works237,207