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Record W2803272798 · doi:10.1080/09540121.2018.1469723

Patient activation among people living with HIV: a cross-sectional comparative analysis with people living with diabetes mellitus

2018· article· en· W2803272798 on OpenAlexafffund
Claire Kendall, Esther S. Shoemaker, Lois M. Crowe, Paul MacPherson, Marissa Becker, Eleni Levreault, Lisa M. Boucher, Ron Rosenes, Christine Bibeau, Philip Lundrigan, Clare Liddy

Bibliographic record

VenueAIDS Care · 2018
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsUniversity of ManitobaOttawa HospitalInstitute for Clinical Evaluative SciencesBruyèreUniversity of OttawaSt. Michael's Hospital
FundersCanadian Institutes of Health Research
KeywordsLogistic regressionMedicineDiabetes mellitusHuman immunodeficiency virus (HIV)Cross-sectional studyDemographicsGerontologyChronic diseaseFamily medicineDemographyInternal medicinePathology

Abstract

fetched live from OpenAlex

Standardized self-management supports are an integral part of care delivery for many chronic conditions. We used the validated Patient Activation Measure (PAM®) to assess level of engagement for self-management from a sample of 165 people living with HIV (PLWH) and 163 people with diabetes. We conducted multivariable logistic regression to assess associations between demographics and PAM® scores. PLWH had high levels of activation that were no different from those of people with diabetes (mean score = 67.2, SD = 14.2 versus 65.0, SD = 14.9, p = 0.183). After adjusting for patient characteristics, only being on disability compared to being employed or a student was associated with being less activated (AOR = 0.276, 95%CI = 0.103-0.742). Our findings highlight the potential for the implementation of existing standardized chronic disease self-management programs to enhance the care delivery for PLWH, with people on disability as potential target populations.

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 score0.007
Threshold uncertainty score0.690

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.010
GPT teacher head0.258
Teacher spread0.247 · 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

Citations7
Published2018
Admission routes2
Has abstractyes

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