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Record W2153841813 · doi:10.1080/09540121.2014.997661

Barriers to health-care and psychological distress among mothers living with HIV in Quebec (Canada)

2015· article· en· W2153841813 on OpenAlexafffundabout
Martin Blais, Mylène Fernet, Karène Proulx‐Boucher, Bertrand Lebouché, Carl Rodrigue, Normand Lapointe, Joanne Otis, Johanne Samson

Bibliographic record

VenueAIDS Care · 2015
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversité de MontréalMcGill University Health CentreCentre Hospitalier Universitaire Sainte-JustineUniversité du Québec à Montréal
FundersCanadian Institutes of Health Research
KeywordsPsychological interventionLogistic regressionDistressClinical psychologyMedicineMultivariate analysisHealth carePsychological distressPsychological resilienceQuality of life (healthcare)Mental healthPsychiatryPsychologyNursingSocial psychology

Abstract

fetched live from OpenAlex

Health-care providers play a major role in providing good quality care and in preventing psychological distress among mothers living with HIV (MLHIV). The objectives of this study are to explore the impact of health-care services and satisfaction with care providers on psychological distress in MLHIV. One hundred MLHIV were recruited from community and clinical settings in the province of Quebec (Canada). Prevalence estimation of clinical psychological distress and univariate and multivariable logistic regression models were performed to predict clinical psychological distress. Forty-five percent of the participants reported clinical psychological distress. In the multivariable regression, the following variables were significantly associated with psychological distress while controlling for sociodemographic variables: resilience, quality of communication with the care providers, resources, and HIV disclosure concerns. The multivariate results support the key role of personal, structural, and medical resources in understanding psychological distress among MLHIV. Interventions that can support the psychological health of MLHIV are discussed.

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 score0.598
Threshold uncertainty score0.666

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 head0.020
GPT teacher head0.325
Teacher spread0.305 · 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

Citations8
Published2015
Admission routes3
Has abstractyes

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