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Record W2091420407 · doi:10.1080/09638280601029985

Personal experience of living with knee osteoarthritis among older adults

2007· article· en· W2091420407 on OpenAlexaff
Monica R. Maly, Terry Krupa

Bibliographic record

VenueDisability and Rehabilitation · 2007
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsQueen's UniversityWestern University
Fundersnot available
KeywordsOsteoarthritisMedicineActivities of daily livingPhysical therapyPhysical medicine and rehabilitationRehabilitationGerontologyPsychologyAlternative medicine

Abstract

fetched live from OpenAlex

PURPOSE: Knee osteoarthritis (OA) is the most common cause of chronic disability amongst community-dwelling older adults. Yet, little is understood about the daily experience of knee OA. As clinicians we fail to understand a large group of individuals that we aim to help. We conducted an exploratory study that aimed to understand the experience of living with knee OA in older adults. METHOD: We used a descriptive phenomenology, grounded in the phenomenology in practice tradition. We conducted nine interviews with participants with physician-diagnosed knee OA, of different ages, sexes, cultural backgrounds and self-perceptions. Ninety-minute interviews with each participant were audio-taped and transcribed verbatim. We used the vanKaam method of phenomenological analysis, modified by Moustakas, as the framework for data analysis. FINDINGS: The following five themes on living with knee OA emerged: experiencing knee pain is central to daily living, experiencing mobility limitations devalues self-worth, sharing the experience, assessing our own health and managing chronic pain. CONCLUSIONS: The implications of these findings highlight the profound impact knee OA has on daily living, which have been poorly documented in the past. Clinicians should consider that the consequences of living with knee OA are significant enough to influence a person's sense of self-worth.

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 score0.133
Threshold uncertainty score0.383

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 studies0.0000.001
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 head0.005
GPT teacher head0.238
Teacher spread0.233 · 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

Citations73
Published2007
Admission routes1
Has abstractyes

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