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Record W2109827367 · doi:10.1123/apaq.26.3.259

Effect of Indoor Wall Climbing on Self-Efficacy and Self-Perceptions of Children with Special Needs

2009· article· en· W2109827367 on OpenAlexafffund
Erin R. Mazzoni, P. Lynn Purves, Julie Southward, Ryan E. Rhodes, Viviene A. Temple

Bibliographic record

VenueAdapted Physical Activity Quarterly · 2009
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsUniversity of Victoria
FundersCanadian Institutes of Health ResearchMichael Smith Health Research BCShriners Hospitals for Children
KeywordsSelf-efficacyPerceptionCompetence (human resources)PsychologyClimbingDevelopmental psychologyPhysical therapyClinical psychologyMedicineSocial psychology

Abstract

fetched live from OpenAlex

The impact of a six-week indoor wall climbing on the perceptions of self for children with special needs aged 6-12 years was explored. Participants (n = 46) were randomly assigned to the intervention (girls, n = 4; boys, n = 19) and control groups (girls, n = 5; boys, n = 18). Belayers' and children's perceptions of efficacy were measured using specifically designed questionnaires and perceptions of competence and global self-worth were measured using Harter's (1985) Self-Perception Profile for Children for participants with an adaptive age of 8 years or higher. Children's self-efficacy and belayers' ratings of children's efficacy improved significantly, t(21) = 3.9, p = .001, d= .84 and F(2, 44) = 30.03, p < .001, respectively. The children's judgments of their athletic and social competence and global self-worth, however, did not change over time or differ from the wait-listed control group (p > .05). These results suggest that it is likely that many experiences that enhance self-efficacy may be needed to improve self-perceptions.

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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.254
Teacher spread0.248 · 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

Citations59
Published2009
Admission routes2
Has abstractyes

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