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Record W4385297955 · doi:10.1080/09540121.2023.2240066

Understanding motivations and resilience-associated factors to promote timely linkage to HIV care: a qualitative study among people living with HIV in western Kenya

2023· article· en· W4385297955 on OpenAlexafffund
Stephanie M. DeLong, Catherine Kafu, Juddy Wachira, Jennifer M. Knight, Paula Braitstein, Don Operario, Becky L. Genberg

Bibliographic record

VenueAIDS Care · 2023
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsPublic Health OntarioUniversity of Toronto
FundersNational Institute of Mental HealthCanadian Institutes of Health Research
KeywordsPsychological resilienceContext (archaeology)PsychologyLinkage (software)Human immunodeficiency virus (HIV)InterviewHealth careQualitative researchSocial supportGerontologyNursingSocial psychologyMedicineFamily medicineSociologyPolitical science

Abstract

fetched live from OpenAlex

Understanding motivations and resilience-associated factors that help people newly diagnosed with HIV link to care is critical in the context of universal test and treat. We analyzed 30 in-depth interviews (IDI) among adults aged 18 and older in western Kenya diagnosed with HIV during home-based counseling and testing and who had linked to HIV care. A directed content analysis was performed, categorizing IDI quotations into a table based on linkage stages for organization and then developing and applying codes from self-determination theory and the concept of resilience. Autonomous motivations, including internalized concerns for one's health and/or to provide care for family, were salient facilitators of accessing care. Controlled forms of motivation, such as fear or external pressure, were less salient. Social support was an important resilience-associated factor fostering linkage. HIV testing and counseling programs which incorporate motivational interviewing that emphasizes motivations related to one's health or family combined with a social support/navigator approach, may promote timely linkage to care.

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)0.001
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 score0.082
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.052
GPT teacher head0.361
Teacher spread0.309 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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