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Record W3044178502 · doi:10.1080/2159676x.2020.1778064

Interpretative Phenomenological Analysis of Community Exercise Experiences after Severe Traumatic Brain Injury

2020· article· en· W3044178502 on OpenAlexaff
Enrico Quilico, William J. Harvey, Jeffrey G. Caron, Gordon A. Bloom

Bibliographic record

VenueQualitative Research in Sport Exercise and Health · 2020
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversité de MontréalMcGill UniversityDouglas Mental Health University InstituteToronto Rehabilitation InstituteUniversity of Toronto
Fundersnot available
KeywordsInterpretative phenomenological analysisTraumatic brain injuryPsychologyRehabilitationPerceptionCognitionAcquired brain injuryClinical psychologyPhysical medicine and rehabilitationMedicineQualitative researchPsychiatry

Abstract

fetched live from OpenAlex

Traumatic brain injury (TBI) is a major public health concern due to its growing incidence and resulting long-term or lifelong impairments. Exercise is a non-stigmatising approach proposed to alleviate the physical, cognitive, social, and emotional consequences after TBI. We used Interpretative Phenomenological Analysis (IPA) to explore the exercise experiences of seven individuals living with a severe TBI, 5–31 years after rehabilitation. We engaged in semi-structured interviews with the participants and we used IPA to explore their post-TBI exercise experiences outside of the clinical setting. Based on our analysis, we found three themes encompassed how TBI-related impairments affected the participants’ abilities, self-perceptions, and perspectives on life. The participants also identified optimal environments for exercise participation, as well as perceived physical, social, and psychological effects of exercise. Future recommendations include developing community-based exercise programmes to assist with social reintegration and exploring the full range of benefits obtainable from exercise after a TBI.

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)0.013
metaresearch head score (Gemma)0.001
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 score0.103
Threshold uncertainty score0.887

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.004
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.440
GPT teacher head0.573
Teacher spread0.133 · 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

Citations17
Published2020
Admission routes1
Has abstractyes

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