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Record W4317041547 · doi:10.1177/23743735231151537

Cultural Knowledge in Context – People Aged 50 Years and Over Make Sense of a First Fracture and Osteoporosis

2023· article· en· W4317041547 on OpenAlexaffabout
Patricia Harasym, Lauren A Beaupré, Angela Juby, Paul Kivi, Sumit R. Majumdar, Heather Hanson

Bibliographic record

VenueJournal of Patient Experience · 2023
Typearticle
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsAlberta Health ServicesHealth Sciences CentreUniversity of AlbertaUniversity of Calgary
Fundersnot available
KeywordsOsteoporosisContext (archaeology)MedicineGerontologyPsychologyPhysical therapy

Abstract

fetched live from OpenAlex

Catch a Break (CaB) is a secondary fracture prevention program that uses medical understandings of osteoporosis to assess first fractures and determine appropriateness for secondary fracture prevention. In this study, we interviewed CaB program participants to identify the understandings that patients themselves used to make sense of first fractures and the osteoporosis suggestion as cause. Semi-structured interviews were conducted with female and male participants of the CaB program in Canada. An interpretive practice approach was used to analyze the data. A random sample of 20 individuals, 12 women, and eight men all aged 50 years and over participated. First fractures were produced as meaningful in the context of osteoporosis only for seniors of very advanced age, and for people of any age with poor nutrition. The trauma events that led to a first fracture were produced as meaningful only if perceived as accidents, and having an active lifestyle was produced as beneficial only for mental health and well-being unrelated to osteoporosis. Cultural knowledge shapes, but does not determine, how individuals make sense of their health and illness experiences. Risk prevention program designers should include patients on the design team and be more aware of the presumptive knowledge used to identify individuals at risk of disease.

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 score0.247
Threshold uncertainty score0.278

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 head0.023
GPT teacher head0.338
Teacher spread0.315 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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