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Record W4310639078 · doi:10.1186/s12966-022-01371-4

Scale-up of the Internet-based Professional Learning to help teachers promote Activity in Youth (iPLAY) intervention: a hybrid type 3 implementation-effectiveness trial

2022· article· en· W4310639078 on OpenAlexaff
David R. Lubans, Taren Sanders, Michael Noetel, Philip D. Parker, Heather McKay, Philip J. Morgan, Jo Salmon, Morwenna Kirwan, Andrew Bennie, Louisa Peralta, Renata Cinelli, Timothy B. Hartwig, James Boyer, Sarah G. Kennedy, Ronald C. Plotnikoff, Vibeke Hansen, Diego Vasconcellos, Jane Lee, Devan Antczak, Chris Lonsdale

Bibliographic record

VenueInternational Journal of Behavioral Nutrition and Physical Activity · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversity of British Columbia
FundersNational Health and Medical Research CouncilMedical Research CouncilNSW Department of EducationNSW Department of Education and Training
KeywordsIntervention (counseling)The InternetScale (ratio)Medical educationPsychologyBehavioural sciencesProfessional developmentRandomized controlled trialApplied psychologyMedicineComputer scienceNursingWorld Wide WebPsychotherapist

Abstract

fetched live from OpenAlex

BACKGROUND: Whole-of-school programs have demonstrated success in improving student physical activity levels, but few have progressed beyond efficacy testing to implementation at-scale. The purpose of our study was to evaluate the scale-up of the 'Internet-based Professional Learning to help teachers promote Activity in Youth' (iPLAY) intervention in primary schools using the RE-AIM framework. METHODS: We conducted a type 3 hybrid implementation-effectiveness study and collected data between April 2016 and June 2021, in New South Wales (NSW), Australia. RE-AIM was operationalised as: (i) Reach: Number and representativeness of students exposed to iPLAY; (ii) Effectiveness: Impact of iPLAY in a sub-sample of students (n = 5,959); (iii) Adoption: Number and representativeness of schools that received iPLAY; (iv) Implementation: Extent to which the three curricular and three non-curricular components of iPLAY were delivered as intended; (v) Maintenance: Extent to which iPLAY was sustained in schools. We conducted 43 semi-structured interviews with teachers (n = 14), leaders (n = 19), and principals (n = 10) from 18 schools (11 from urban and 7 from rural locations) to determine program maintenance. RESULTS: Reach: iPLAY reached ~ 31,000 students from a variety of socio-economic strata (35% of students were in the bottom quartile, almost half in the middle two quartiles, and 20% in the top quartile). EFFECTIVENESS: We observed small positive intervention effects for enjoyment of PE/sport (0.12 units, 95% CI: 0.05 to 0.20, d = 0.17), perceptions of need support from teachers (0.26 units, 95% CI: 0.16 to 0.53, d = 0.40), physical activity participation (0.28 units, 95% CI: 0.10 to 0.47, d = 0.14), and subjective well-being (0.82 units, 95% CI: 0.32 to 1.32, d = 0.12) at 24-months. Adoption: 115 schools received iPLAY. IMPLEMENTATION: Most schools implemented the curricular (59%) and non-curricular (55%) strategies as intended. Maintenance: Based on our qualitative data, changes in teacher practices and school culture resulting from iPLAY were sustained. CONCLUSIONS: iPLAY had extensive reach and adoption in NSW primary schools. Most of the schools implemented iPLAY as intended and effectiveness data suggest the positive effects observed in our cluster RCT were sustained when the intervention was delivered at-scale. TRIAL REGISTRATION: ACTRN12621001132831.

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)0.011
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0020.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.252
GPT teacher head0.590
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 designNon-randomized trial
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

Citations11
Published2022
Admission routes1
Has abstractyes

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