Predictors of Mental Health Literacy in a Sample of Health Care Major Students: Pilot Evaluation Study.

COVID-19 awareness digital health digital health literacy disorder empathy health literacy literacy mental health mental health literacy mentalizing questionnaire students

Journal

JMIR formative research
ISSN: 2561-326X
Titre abrégé: JMIR Form Res
Pays: Canada
ID NLM: 101726394

Informations de publication

Date de publication:
08 Feb 2024
Historique:
received: 24 10 2022
accepted: 06 10 2023
revised: 04 10 2023
medline: 8 2 2024
pubmed: 8 2 2024
entrez: 8 2 2024
Statut: epublish

Résumé

The numerous mental health awareness campaigns during the COVID-19 pandemic have shifted our understanding and perception of mental health. The purpose of this study is to evaluate predictors of mental health literacy (MHL), that is, one's knowledge and beliefs about mental disorders. We evaluate whether digital health literacy, empathy, and mentalizing contribute to MHL. Our sample consisted of 89 health care major students, aged between 17 and 32 years, studying at a university in Lebanon. The Mental Health Literacy Scale for Healthcare Students (MHLS-HS), the eHealth Literacy Questionnaire (eHLQ), the Basic Empathy Scale (BES), and the Reflective Functioning Questionnaire-8 (RFQ-8) were used. Multiple regression analyses revealed that the Engagement in Own Health subscale of digital health literacy constituted a predictor of MHL. While empathy and mentalizing did not directly predict MHL, they were found to predict components of MHL. This is the first study to evaluate digital health literacy, empathy, and mentalizing as predictors of MHL in Lebanon, a country where mental health is still considered taboo. Moreover, this pilot study is the first to provide some support for the predictive role of some digital health literacy subscales on MHL in light of the rise of the digital era following the COVID-19 pandemic.

Sections du résumé

BACKGROUND BACKGROUND
The numerous mental health awareness campaigns during the COVID-19 pandemic have shifted our understanding and perception of mental health.
OBJECTIVE OBJECTIVE
The purpose of this study is to evaluate predictors of mental health literacy (MHL), that is, one's knowledge and beliefs about mental disorders. We evaluate whether digital health literacy, empathy, and mentalizing contribute to MHL.
METHODS METHODS
Our sample consisted of 89 health care major students, aged between 17 and 32 years, studying at a university in Lebanon. The Mental Health Literacy Scale for Healthcare Students (MHLS-HS), the eHealth Literacy Questionnaire (eHLQ), the Basic Empathy Scale (BES), and the Reflective Functioning Questionnaire-8 (RFQ-8) were used.
RESULTS RESULTS
Multiple regression analyses revealed that the Engagement in Own Health subscale of digital health literacy constituted a predictor of MHL. While empathy and mentalizing did not directly predict MHL, they were found to predict components of MHL.
CONCLUSIONS CONCLUSIONS
This is the first study to evaluate digital health literacy, empathy, and mentalizing as predictors of MHL in Lebanon, a country where mental health is still considered taboo. Moreover, this pilot study is the first to provide some support for the predictive role of some digital health literacy subscales on MHL in light of the rise of the digital era following the COVID-19 pandemic.

Identifiants

pubmed: 38329801
pii: v8i1e43770
doi: 10.2196/43770
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e43770

Informations de copyright

©Pia Tohme, Nour Abi Fadel, Nour Yaktine, Rudy Abi-Habib. Originally published in JMIR Formative Research (https://formative.jmir.org), 08.02.2024.

Auteurs

Pia Tohme (P)

Department of Social and Education Sciences, Lebanese American University, Beirut, Lebanon.

Nour Abi Fadel (N)

Department of Social and Education Sciences, Lebanese American University, Beirut, Lebanon.

Nour Yaktine (N)

Department of Psychology, Saint Joseph University of Beirut, Beirut, Lebanon.
American University of Beirut, Beirut, Lebanon.

Rudy Abi-Habib (R)

Department of Social and Education Sciences, Lebanese American University, Beirut, Lebanon.

Classifications MeSH