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Record W3014097391 · doi:10.1080/01942638.2020.1746946

Player and Parent Experiences with Child and Adolescent Power Soccer Sport Participation

2020· article· en· W3014097391 on OpenAlexaff
Elaine Bragg, Nancy Spencer-Cavaliere, Shanon Phelan, Lesley Pritchard

Bibliographic record

VenuePhysical & Occupational Therapy In Pediatrics · 2020
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsWheelchairAthletesPsychologyPower (physics)RehabilitationApplied psychologyMedical educationMedicinePhysical therapyPolitical science

Abstract

fetched live from OpenAlex

Power soccer presents opportunities for young athletes who use power wheelchairs to experience competitive team sports. As the focus of rehabilitation is to enhance participation and quality of life, insight into the subjective experience of sport participation could broaden considerations for power wheelchair prescription and inform how therapists share information about community sports and other activities with families.Purpose To provide insight into the experiences of power soccer players and their parents to inform rehabilitation practice.Methods Primary data for this Interpretive Description study were individual interviews with five power soccer athletes, ranging from 11 to 17 years of age, and three parents of power soccer players. Observational field notes were also used.Results Five inter-related themes were developed: 1) Level playing field, 2) I am an athlete, 3) Important “life lessons” are gained through team sports, 4) The value of belonging to a community, and 5) Role of the rehabilitation community in supporting power mobility sports.Conclusions Findings of this study demonstrate the benefits and challenges of power sport participation. The results encourage therapists to share information about sport opportunities with families and to consider a broad range of contexts when assessing for power mobility.

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 score0.009
Threshold uncertainty score0.329

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 head0.036
GPT teacher head0.338
Teacher spread0.302 · 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".

Quick stats

Citations14
Published2020
Admission routes1
Has abstractyes

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