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Record W4402191654 · doi:10.1186/s12913-024-11418-w

Developing community-based physical activity interventions and recreational programming for children in rural and smaller urban centres: a qualitative exploration of service provider and parent experiences

2024· article· en· W4402191654 on OpenAlexafffund
Emma Ostermeier, Jason Gilliland, Jennifer D. Irwin, Jamie A. Seabrook, Patricia Tucker

Bibliographic record

VenueBMC Health Services Research · 2024
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsChildren’s Health Research InstituteLondon Health Sciences CentreLawson Health Research InstituteWestern University
FundersLawson Foundation
KeywordsRecreationService providerPsychological interventionPublic healthNursing researchHealth promotionContext (archaeology)MedicinePromotion (chess)Health administrationQualitative researchNursingService (business)Public relationsMedical educationGerontologySociologyBusinessMarketingGeographyPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Children's physical inactivity is a persisting international public health concern. While there is a large body of literature examining physical activity interventions for children, the unique physical activity context of low-density communities in rural areas and smaller urban centres remains largely underexplored. With an influx of families migrating to rural communities and small towns, evaluations of health promotion efforts that support physical activity are needed to ensure they are meeting the needs of the growing populations in these settings. The aim of this community-based research was to explore service providers' and parents' perspectives on physical activity opportunities available in their community and recommendations toward the development and implementation of efficacious physical activity programming for children in rural communities and smaller urban centres. METHODS: Three in-person community forums with recreation service providers (n = 37 participants) and 1 online community forum with the parents of school-aged children (n = 9 participants) were hosted. An online survey and Mentimeter activity were conducted prior to the community forums to gather participants' views on the barriers and facilitators to physical activities and suggestions for activity-promoting programs. The service provider and parent discussions were audio-recorded, transcribed verbatim, and analyzed following a deductive approach guided by Hseih and Shannon's (2005) procedure for direct content analysis. A code list developed from the responses to the pre-forum survey and Mentimeter activity was used to guide the analysis and category development. RESULTS: Seven distinct categories related to the existing physical activity opportunities and recommendations for programs in rural communities and smaller urban centres were identified during the analysis: (1) Recovery from Pandemic-Related Measures, (2) Knowledge and Access to Programs, (3) Availability, (4) Personnel Support, (5) Quality of Programs and Facilities, (6) Expenses and Subsidies, and (7) Inclusivity and Preferences. CONCLUSION: To improve the health and well-being of children who reside in low-density areas, the results of this study highlight service provider and parent recommendations when developing and implementing community-based physical activity programs and interventions in rural and smaller urban settings, including skill development programs, non-competitive activity options, maximizing existing spaces for activities, and financial support.

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)0.009
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0080.006
Scholarly communication0.0030.002
Open science0.0020.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.240
GPT teacher head0.515
Teacher spread0.274 · 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 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

Citations5
Published2024
Admission routes2
Has abstractyes

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