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Record W2011915323 · doi:10.1016/s1474-5151(09)60015-3

26 Men's Everyday Tactics for Balancing Diabetes Self Care and Cardiac Rehabilitation

2009· article· en· W2011915323 on OpenAlexaffabout
Craig Dale, Jan Angus, Alexander M. Clark, Jennifer Lapum, Susan Marzolini, Marnie Kramer, Lisa Seto, Paul Oh, Jayne Price, Beth L. Abramson

Bibliographic record

VenueEuropean Journal of Cardiovascular Nursing · 2009
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsSt. Michael's HospitalUniversity of TorontoToronto Metropolitan UniversityUniversity of AlbertaWomen's College HospitalToronto Rehabilitation Institute
Fundersnot available
KeywordsMedicineRehabilitationDiabetes mellitusPhysical therapySelf carePhysical medicine and rehabilitationGerontologyHealth care

Abstract

fetched live from OpenAlex

Purpose: Despite the benefits of cardiac rehabilitation (CR) referral, enrolment and attendance rates remain low. Diabetic CR patients are linked to higher rates of attrition and inferior outcomes. Even though men represent the majority of participants in CR, the influence of masculinity has not been well explored. This paper reports findings of a qualitative study of contextual and gender based differences in participation and adherence to diabetic CR recommendations. Methods: We will draw on masculinity studies to understand social circumstances, barriers, resources and strategies for diabetic men in CR. This study is part of a qualitative investigation of 16 men and 16 women recruited in three urban CR programs in Toronto, Canada. Data sources include semi-structured interviews after one month of CR attendance, a one-week activity journal, and a second interview to reflect on the events recorded in the journal. During both interviews, participants elaborate on the specific circumstances, issues and agency involved in managing multiple conditions.

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)0.003
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.975
Threshold uncertainty score0.628

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.007
GPT teacher head0.270
Teacher spread0.263 · 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 designOther design
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

Citations0
Published2009
Admission routes2
Has abstractyes

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