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Record W4243859198 · doi:10.32920/ryerson.14647407.v1

Health Beliefs and Medication Taking Behaviour of Individuals Living with Mild to Moderate Chronic Kidney Disease

2021· preprint· en· W4243859198 on OpenAlexaff
Adelaide KL Hui

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicMedication Adherence and Compliance
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPillKidney diseaseMedicineMedication adherenceDiseaseRegimenHealth careGerontologyPsychologyClinical psychologyFamily medicineNursingInternal medicine

Abstract

fetched live from OpenAlex

The aims of this secondary analysis were to describe medication taking behaviour and health beliefs among people with mild to moderate CKD, examine differences in health beliefs according to age and gender, and examine relationships between health beliefs and medication taking behaviour. The sample consisted of 30 men and 30 women between19 and 72 years old. Forty-two participants reported they did not miss medication doses, but remembering to take all the pills was the most challenging. Women were more likely to believe their kidney function would improve in the future and to believe treatment would keep them from becoming ill. No statistically significant differences were found in health beliefs by age. Perceived barriers were the strongest indicator of medication taking behaviour. Findings from this study shed light on the complexity of the medication regimen in CKD, and could guide health care providers to better support medication taking behaviour.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.049
GPT teacher head0.343
Teacher spread0.294 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2021
Admission routes1
Has abstractyes

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