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Record W2784028605 · doi:10.1123/japa.2017-0103

“I Just Roll Over, Pick Myself Up, and Carry On!” Exploring the Fall-Risk Experience of Canadian Masters Athletes

2018· article· en· W2784028605 on OpenAlexaffabout
Dylan Brennan, Aleksandra Zecevic, Shannon L. Sibbald, Volker Nolte

Bibliographic record

VenueJournal of Aging and Physical Activity · 2018
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsWestern University
Fundersnot available
KeywordsAthletesPsychologyFalling (accident)Fear of fallingInterpretative phenomenological analysisPsychological resilienceSuicide preventionApplied psychologyGerontologyPoison controlPhysical therapySocial psychologyMedicineQualitative researchPsychiatryMedical emergencySociology

Abstract

fetched live from OpenAlex

OBJECTIVES: The risk of falling increases in adults aged 65 years and older. A common barrier to take up physical activity in sedentary older adults is the fear of falls and injury. Experiences of master athletes can provide insights into management of the risk of falling. The purpose of this phenomenological study was to explore the fall-risk experience of masters athletes actively competing in sport. METHODS: Masters athletes aged 55 years and older (N = 22) described their experiences in semistructured interviews. Data were analyzed through an interpretive-constructivist paradigm using inductive content analysis. RESULTS: Five dominant themes emerged: acceptance, learning, awareness, resilience, and self-fulfillment. Participants of this study reported an acceptance of the risk they take in sport for falls and injuries in their pursuits for self-fulfillment. DISCUSSION: Findings indicate that master athletes accept the risk for falls and injuries in sport, find ways to adapt, and continue to compete because it is self-fulfilling. Sharing their experiences might inspire other older adults to get active as a rewarding means of remaining independent.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.637
Threshold uncertainty score0.994

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 integrity0.0000.001
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 head0.066
GPT teacher head0.353
Teacher spread0.287 · 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

Citations7
Published2018
Admission routes2
Has abstractyes

Explore more

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