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Record W4385641688 · doi:10.1123/jtpe.2022-0150

Quality of Life in Individuals With Disabilities Through a Student-Led Service-Learning Program: Qualitative and Quantitative Analysis to Examine the Reciprocal Benefits of Service Learning

2023· article· en· W4385641688 on OpenAlexaff
Mai Narasaki‐Jara, Donald James Brolsma, Katira Abdolrazagh, Kai Sun, Masahiro Yamada, Aya Mitani, Taeyou Jung

Bibliographic record

VenueJournal of Teaching in Physical Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicInclusion and Disability in Education and Sport
Canadian institutionsPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsPsychologyIntervention (counseling)Medical educationQuality of life (healthcare)Learning disabilityAnxietyMental healthApplied psychologyGerontologyDevelopmental psychologyMedicine

Abstract

fetched live from OpenAlex

Purpose : The efficacy of service learning in physical activity (PA) intervention is generally not centered around perceptions of the service recipients, posing questions when the efficacy of an intervention is crucial, such as PA in people with disabilities. The present study examined perceptions of the recipients in a student-led service-learning program through a quality of life survey and interviews. Method : Undergraduate students led a 13-week PA intervention. Before and after the intervention, people with disabilities ( N = 56) completed quality of life surveys (i.e., the National Institute of Health Patient-Reported Outcomes Measurement Information System). A face-to-face interview was conducted with N = 6. Results : All quality of life items, except for Mental Health–Anxiety, improved ( p < .01). The interviews suggested that environmental factors (i.e., encouragement from students, the open space that allowed interactions) positively impacted the program. Discussion : The environment may be a key factor in the program’s success in PA intervention from the recipients’ perspective.

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)0.006
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation 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 score0.082
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.085
GPT teacher head0.489
Teacher spread0.403 · 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 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

Citations1
Published2023
Admission routes1
Has abstractyes

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