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Record W4315619171 · doi:10.3928/24748307-20221215-01

Health Literacy and Serious or Persistent Mental Illness: A Mixed Methods Study

2023· article· en· W4315619171 on OpenAlexfundno aff
Allen McLean, Donna Goodridge, James Stempien, Douglas Harder, Nathaniel Osgood

Bibliographic record

VenueHLRP Health Literacy Research and Practice · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsnot available
FundersRoyal University Hospital Foundation
KeywordsHealth literacyMental healthMental illnessMedicinePsychological interventionHealth careScale (ratio)PsychologyGerontologyPsychiatry

Abstract

fetched live from OpenAlex

Background: Health literacy is increasingly recognized as a major determinant of health; however, our insights into the health literacy strengths and needs of adults living with serious or persistent mental illness remain limited by a notable lack of research in this area. Improving our understanding is important because people in this group are especially vulnerable to numerous negative health outcomes, many preventable. Objective: To assess the health literacy strengths and needs of people living with serious or persistent mental illness in terms of their ability to acquire, understand, and use information about their illness and the health services they require. Methods: A cross-sectional convergent mixed methods design guided by the Ophelia Access and Equity Framework. People diagnosed with serious or persistent mental illness were offered participation. Quantitative and qualitative data was collected using questionnaires (Health Literacy Questionnaire [HLQ], World Health Organization [WHO-5]) and semi-structured interviews. Hierarchical cluster analysis identified and grouped participants with similar health literacy scores into mutually exclusive groups, for the development of clinical vignettes. Key Results: Participants struggled most with the appraisal of health information (HLQ mean 2.72, standard deviation [ SD ] .63 [scale 1–4]) and navigating what they often perceived to be a confusing health care system (HLQ mean 3.29, SD .79 [scale 1–5]). On the other hand, most participants reported positive experiences with their health care providers (HLQ mean 3.19, SD .62 [scale 1–4]) and generally felt understood and supported. The cluster analysis suggests we should not assume people living with serious or persistent mental illness have homogeneous HL strengths and needs, meaning a one-size-fits-all solution for improving health literacy in this diverse group will likely not be a successful strategy. It will be important to explore solutions that embrace patient-centered care approaches. Conclusions: This study is one of only a handful assessing the health literacy strengths and needs of people living with serious or persistent mental illness. By collecting both quantitative and qualitative data, then analyzing the results using sophisticated cluster analysis methods, the authors were able to develop clinical vignettes per the Ophelia Framework that offer results in a practical way that can be readily understood and acted upon by stakeholders. We found that the HLQ is a measure of HL that is acceptable to mental health clients, and our findings provide preliminary data on the use of this instrument in the mental health population. [ HLRP: Health Literacy Research and Practice . 2023;7(1):e2–e13. ] Plain Language Summary: This study explored the health literacy strengths and needs of people living with serious or persistent mental illness. The results showed a mix of strengths and needs among our participants, though several consistent themes emerged. Most of our participants felt understood and supported by their health care providers, but many often struggle with judging the quality of health information and finding their way through the health care system.

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)0.076
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.808
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0760.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0080.000
Scholarly communication0.0010.006
Open science0.0000.001
Research integrity0.0000.003
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 head0.260
GPT teacher head0.662
Teacher spread0.402 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations3
Published2023
Admission routes1
Has abstractyes

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