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Record W2788444452 · doi:10.3138/ptc.2017-01.ep

“A Learned Soul to Guide Me”: The Voices of Those Living with Kidney Disease Inform Physical Activity Programming

2018· article· en· W2788444452 on OpenAlexafffundvenueabout
Trisha Parsons, Clara Bohm, Katherine Poser

Bibliographic record

VenuePhysiotherapy Canada · 2018
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsHealth Sciences CentreQueen's University
FundersKidney Foundation of Canada
KeywordsSoulComputer scienceMedicinePhysical medicine and rehabilitationGerontologyEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

Purpose: The purpose of this study was to (a) confirm the barriers to and facilitators of physical activity (PA) among persons living with chronic kidney disease (CKD) in Ontario and (b) inform the design of a Kidney Foundation of Canada Active Living for Life programme for persons living with CKD. Method: Adults living with CKD in Ontario were invited to participate in a cross-sectional survey investigating opinions about and needs for PA programming. The 32-item survey contained four sections: programme delivery preferences, current PA behaviour, determinants of PA, and demographics. Data were summarized using descriptive statistics and thematic coding. Results: A total of 63 respondents participated. They had a mean age of 56 (SD 16) years, were 50% female, and were 54% Caucasian; 66% had some post-secondary education. The most commonly reported total weekly PA was 90 minutes (range 0–1,050 minutes). Most respondents (84%) did not regularly perform strength training, and 73% reported having an interest in participating in a PA programme. Conclusion: Individuals living with CKD require resources to support and maintain a physically active lifestyle. We identified a diversity of needs, and they require a flexible and individualized inter-professional strategy that is responsive to the episodic changes in health status common in this population.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.621
Threshold uncertainty score0.818

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 head0.024
GPT teacher head0.350
Teacher spread0.326 · 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

Citations14
Published2018
Admission routes4
Has abstractyes

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