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Record W2769516646 · doi:10.2196/resprot.8080

The Association of Health Literacy and Electronic Health Literacy With Self-Efficacy, Coping, and Caregiving Perceptions Among Carers of People With Dementia: Research Protocol for a Descriptive Correlational Study

2017· article· en· W2769516646 on OpenAlexvenueno aff
Areti Efthymiou, Nicos Middleton, Andreas Charalambous, Evridiki Papastavrou

Bibliographic record

VenueJMIR Research Protocols · 2017
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsnot available
FundersUniversity of CyprusCyprus University of Technology
KeywordseHealthHealth literacyDementiaLiteracyPsychologyCoping (psychology)GerontologyMedicineHealth careNursingClinical psychologyDisease

Abstract

fetched live from OpenAlex

BACKGROUND: In the last decade, electronic health (eHealth) literacy has attracted the attention of the scientific community, as it is associated with the self-management of patients with chronic diseases and the quality and cost of care. It is estimated that 80% of people with chronic diseases are cared for at home by a family member, friend, or relative. Informal carers are susceptible to physical and mental health problems, as well as social and financial hardships. Nevertheless, there seems to be a research gap in terms of carers' needs, skills, and available resources in the age of new technologies, with the vital role of eHealth literacy of the carers remaining unexplored. OBJECTIVE: The aim of this study was to investigate the level of eHealth literacy and health literacy of primary and secondary carers of people with dementia, to explore the association between health and eHealth literacy, as well as their association with the caregiving variables: self-efficacy, coping, and caring perceptions. METHODS: A sample of 200 primary carers (the carer who supports the people with dementia in everyday living) and 200 secondary carers (family member, friend, or other person in the social network assisting the primary carer in their role) will be recruited from dementia day care centers and Alzheimer's associations in Greece and Cyprus. The study will be a cross-sectional correlational descriptive study. Tools to be used include the eHealth Literacy Scale adapted for carers to measure eHealth literacy, European Health Literacy Survey Questionnaire 16 (HLS-EU-Q16), Single Item Literacy Screener, Revised Scale for Caregiving Self-Efficacy, Carers of Older People in Europe (COPE) index for caregiving perceptions, and COPE brief to measure selected coping strategies. Descriptive statistics will be reported, and correlations between different variables will be explored with parametric and nonparametric measures. RESULTS: As a preliminary study, the HLS-EU-Q16 has been validated in 107 older people. The internal consistency of the scale as estimated using Cronbach alpha coefficient was .77, somewhat lower than other validation studies. Recruitment of pilot study participants started in May 2017. CONCLUSIONS: Carers' eHealth literacy is a new field. Whereas previous studies have focused on the role and impact of low eHealth literacy and health literacy among older adults, the eHealth literacy of carers, and in fact carers of people with dementia, has not been explored. We hypothesize an association between eHealth literacy and health literacy level with carers' perceptions about caregiving role, self-efficacy, and coping strategies. A possible moderator in these associations is the secondary carers' eHealth and health literacy level, which will also be explored. By confirming the above hypotheses, tailored eHealth literacy interventions for carers of people with dementia and their families will be developed as a direct outcome of this research.

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)0.030
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.190
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0300.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0100.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.002
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 head0.194
GPT teacher head0.619
Teacher spread0.425 · 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 designObservational
Domainnot available
GenreProtocol

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

Citations46
Published2017
Admission routes1
Has abstractyes

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