Dataset Information

Self-efficacy assessment tools for individuals with depression: protocol for a systematic review of the literature.

ABSTRACT:

Introduction

Depression is a common debilitating disease that affects individuals in all age groups. The impact of the diagnosis extends beyond the individual, with negative effects on mental health, physical health and social well-being. Self-efficacy has been referenced as an important aspect to the prognosis of mood disorders by conferring co-responsibility to the affected individual to face his/her health problems. Several assessment tools are found in the literature for measuring self-efficacy, but it is not yet clear which of these measures are more applicable to individuals with mood disorders, particularly depression. Thus, the aim of present study is to propose a systematic review to examine the psychometric properties and applicability of assessment tools designed to measure self-efficacy in individuals with symptoms and/or a diagnosis of depression.

Methods and analysis

This protocol is reported in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols statement and the review will be reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. The evaluation of the psychometric properties of the health outcome measures will be conducted according to COSMIN guidelines. Two independent reviewers will perform the electronic searches in the PubMed, Web of Science, PsycInfo, SCOPUS and CINAHL databases, followed by the use of the 'snowball' strategy. The inclusion criteria will be (1) instrument validation studies, (2) developed with individuals of any age (3) with symptoms or a diagnosis of depression. Two independent reviewers will analyse the titles and abstracts of the articles retrieved during the search for pre-selection, followed by full-text analyses to determine inclusion in the review based on the eligibility criteria. Cases of a divergence of opinion will be resolved by a third reviewer. Descriptive analysis of the articles will be performed (data on participants, characteristics, psychometric properties and clinical usefulness of the assessment tools).

Ethics and dissemination

The proposed systematic review will provide information on assessment tools employed to measure self-efficacy with regard to coping with depression, offering data on the psychometric properties, strong and weak points, and clinical applicability. As a secondary analysis of the literature, the approval of an ethics committee is not required.

Prospero registration number

CRD42017078707.

SUBMITTER: Rosas MA

PROVIDER: S-EPMC6589040 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Publications

Self-efficacy assessment tools for individuals with depression: protocol for a systematic review of the literature.

Rosas Marina Araújo MA Cordeiro Tattiana Dias de Carvalho TDC Silva Tatiana de Paula Santana da TPSD Caldas Ada Salvetti Cavalcanti ASC Ribeiro Carlos Eduardo de Souza Leão CESL Marinho Manoel Henrique da Nóbrega MHDN Lima Murilo Duarte da Costa MDDC Vasconcelos Selene Cordeiro SC

BMJ open 20190618 6

<h4>Introduction</h4>Depression is a common debilitating disease that affects individuals in all age groups. The impact of the diagnosis extends beyond the individual, with negative effects on mental health, physical health and social well-being. Self-efficacy has been referenced as an important aspect to the prognosis of mood disorders by conferring co-responsibility to the affected individual to face his/her health problems. Several assessment tools are found in the literature for measuring se ...[more]

PMID: 31217313

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Dataset Information

Self-efficacy assessment tools for individuals with depression: protocol for a systematic review of the literature.

Introduction

Methods and analysis

Ethics and dissemination

Prospero registration number

Publications

Self-efficacy assessment tools for individuals with depression: protocol for a systematic review of the literature.

Similar Datasets

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