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Record W4411738788 · doi:10.1080/17408989.2025.2524823

A decade of impact: sustainability and effectiveness of 10 years continuous delivery of a motor competence program in a girls’ secondary school

2025· article· en· W4411738788 on OpenAlexaboutno aff
Natalie Lander, Vicki Hoban, Lisa M. Barnett, Jiani Ma, Johannes Carl, Simone J.J.M. Verswijveren, Jo Salmon

Bibliographic record

VenuePhysical Education and Sport Pedagogy · 2025
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsnot available
FundersAustralian Research Council
KeywordsSustainabilityCompetence (human resources)PsychologyMedical educationMedicineSocial psychology

Abstract

fetched live from OpenAlex

Background: Motor competence (MC), an umbrella term for movement skills, motor coordination, and/or stability, is an important contributor to child health and development. Despite its importance, MC levels among children are low, and declining. Further, girls often display lower levels of MC, particularly object control skills, compared to boys. Interventions targeting girls’ MC are critical, and implementation sustainability is crucial for the long-term success of interventions. Yet there is a lack of literature on the sustainability of MC programs in real-world settings. The aim of this current study was to investigate the sustainability and effectiveness of 10 years continuous delivery of a MC program in an Australian girls’ secondary school. Specifically, this study aimed to (i) investigate the impact of the program on girls’ MC, each year, and trends over time and (ii) explore reasons for long-term program sustainability from the perspectives of school leaders and teachers.Method A convergent mixed-methods design was employed. MC data were collected annually from Year 7 girls (aged 12–13) from 2015 to 2024 using the Canadian Agility and Movement Skill Assessment, pre and post semester-long (approximately 22 weeks) program. Outcomes were analyzed using general linear models with repeated measures to assess pre- and post-program changes, and cohort-based comparisons over 10 years, incorporating both linear and quadratic models to identify the best fit. In 2024, online in-depth one-on-one interviews with school leaders and teachers (n = 7) explored perceptions of the program's sustainability and its ongoing impact on student, teacher, and school level factors. An inductive/deductive hybrid thematic analysis was used to analyze the interviews.Results Valid pre- and post-program data were collected from 868 students, with annual cohort sizes ranging from 73 to 100 students. All students were female and aged between 12 and 13 years. General linear modeling indicated significant improvement in MC, with Year 7 girls showing a large effect from pre- (M = 18.20, SD = 3.82) to post-program (M = 21.92, SD = 3.23), F(1) = 1380, p < .001, η2 = .617. A quadratic relationship was found between lower starting values and greater improvements (p < .001, R2 = .350). Interviews illuminated the ongoing impact of the program on student's MC, particularly those with lower MC. Factors such as strong leadership support, school champion, stakeholder engagement, and data-driven insights influenced sustainability. Convergent interpretations revealed two key findings: improved outcomes for all and the greatest outcomes for the most in need.Conclusion This study demonstrates the sustained effectiveness of the program, particularly for Year 7 girls with lower MC. The program's success was supported by its adaptability, data-driven approach, strong leadership, and active teacher involvement, which facilitated its integration into the school. The findings emphasize the importance of aligning the program with the school's core values to secure ongoing leadership support, teacher commitment, and student engagement, ensuring its long-term sustainability.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.293
Threshold uncertainty score0.412

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.007
GPT teacher head0.354
Teacher spread0.348 · 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 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".

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Citations1
Published2025
Admission routes1
Has abstractyes

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