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Record W2109549141 · doi:10.1186/s12913-014-0614-1

Health literacy: health professionals’ understandings and their perceptions of barriers that Indigenous patients encounter

2014· article· en· W2109549141 on OpenAlexafffundabout
Michelle Lambert, Joanne Luke, Bernice Downey, Sue Crengle, Margaret Kelaher, Susan Reid, Janet Smylie

Bibliographic record

VenueBMC Health Services Research · 2014
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsPublic Health OntarioUniversity of TorontoSt. Michael's Hospital
FundersHealth Research Council of New ZealandMcMaster UniversityNational Health and Medical Research CouncilMedical Research CouncilCanadian Institutes of Health ResearchUniversity of Toronto
KeywordsHealth literacyThematic analysisHealth careMedicineIndigenousHealth equityNursingFocus groupHealth policyPublic healthLiteracyDisadvantagedHealth administrationHealth educationQualitative researchPsychologySociologyPolitical sciencePedagogySocial science

Abstract

fetched live from OpenAlex

BACKGROUND: Despite the growing interest in health literacy, little research has been done around health professionals' knowledge of health literacy or understandings of the barriers to health literacy that patients face when navigating the health care system. Indigenous peoples in New Zealand (NZ), Canada and Australia experience numerous inequalities in health status and outcomes and international evidence reveals that Indigenous, minority, and socio-economically disadvantaged populations have greater literacy needs. To address concerns in Indigenous health literacy, a two-pronged approach inclusive of both education of health professionals, and structural reform reducing demands the system places on Indigenous patients, are important steps towards reducing these inequalities. METHODS: Four Indigenous health care services were involved in the study. Interviews and one focus group were employed to explore the experiences of health professionals working with patients who had experienced cardiovascular disease (CVD) and were taking medications to prevent future events. A thematic analysis was completed and these insights were used in the development of an intervention that was tested as phase two of the study. RESULTS: Analysis of the data identified ten common themes. This paper concentrates on health professionals' understanding of health literacy and perceptions of barriers that their patients face when accessing healthcare. Health professionals' concepts of health literacy varied and were associated with their perceptions of the barriers that their patients face when attempting to build health literacy skills. These concepts ranged from definitions of health literacy that were focussed on patient deficit to broader definitions that focussed on both patients and the health system. All participants identified a combination of cultural, social and systemic barriers as impediments to their Indigenous patients improving their health literacy knowledge and practices. CONCLUSIONS: This study suggests that health professionals have a limited understanding of health literacy and of the consequences of low health literacy for their Indigenous patients. This lack of understanding combined with the perceived barriers to improving health literacy limit health professionals' ability to improve their Indigenous patients' health literacy skills and may limit patients' capacity to improve understanding of their illness and instructions on how to manage their health condition/s.

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)0.006
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.004
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.002
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 head0.068
GPT teacher head0.495
Teacher spread0.427 · 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 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

Citations121
Published2014
Admission routes3
Has abstractyes

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