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Record W3207363517 · doi:10.1177/10497323211040769

Chronic Illness Management in Culturally and Linguistically Diverse Patients: Exploring the Needs, Access, and Understanding of Information

2021· article· en· W3207363517 on OpenAlexafffundabout
Sylvie Lambert, Katya Loban, Jane Li, Tracy Nghiem, Jamie Schaffler, Christine Maheu, Sylvie Dubois, Nathalie Folch, Elisa Gélinas-Phaneuf, Andréa Maria Laizner

Bibliographic record

VenueQualitative Health Research · 2021
Typearticle
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsMcGill University Health CentreCentre Hospitalier de l’Université de MontréalMcGill University
FundersCanadian Institutes of Health ResearchCanada Research ChairsRéseau de recherche portant sur les interventions en sciences infirmières du Québec
KeywordsPsychosocialMedicineInformation needsQualitative researchNursingPerceptionHealth literacyHealth carePsychologyPsychiatry

Abstract

fetched live from OpenAlex

In Canada, people from culturally and linguistically diverse (CALD) backgrounds are at a greater risk of developing a chronic illness, and are more likely to experience adverse health effects and challenges in accessing high-quality care compared with Canadian-born individuals. This, in part, has been attributed to having inadequate access to information and resources needed to manage their illness(es). A qualitative descriptive design and inductive content analysis were used to explore the information needs of 24 CALD patients with chronic illnesses. Participants identified medical, lifestyle, and psychosocial information needs. How much information was needed depended on such antecedents as illness trajectory, severity, and perception. Most information needs remained unmet. A number of communication strategies were identified to bridge language barriers that go beyond translation and are based on effective health education strategies. Findings can help health care professionals better identify CALD patients' information needs and provide strategies that go beyond translation.

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)0.007
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.393
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.607
GPT teacher head0.620
Teacher spread0.013 · 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.

Study designQualitative
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

Citations21
Published2021
Admission routes3
Has abstractyes

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