INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025

Identification of Factors Causing Low Resilience in Post-Stroke

Patients: A Systematic Review

Izzudin Muhammad Firas*, Suhadianto, IGAA Noviekayati

Universitas 17 Agustus 1945 Surabaya, Indonesia

*Corresponding Author

DOI: https://dx.doi.org/10.47772/IJRISS.2025.91100505

Received: 06 December 2025; Accepted: 12 December 2025; Published: 20 December 2025

ABSTRACT

Stroke is the leading cause of long-term disability worldwide, significantly affecting patients' physical, cognitive,

and psychological abilities. Psychological resilience, as an adaptive capacity, is crucial for post-stroke patients

to recover function and quality of life. However, many stroke survivors exhibit low levels of resilience, which

implies an increased risk of depression, anxiety, and decreased rehabilitation compliance. Understanding the

most consistent determinants of resilience is a crucial step in developing targeted interventions. This study aims

to identify and synthesize the main causal factors contributing to low levels of psychological resilience in post-

stroke patients, based on the latest empirical evidence from the scientific literature. This systematic review

follows the PRISMA guidelines. Literature searches were conducted in electronic databases such as Google

Scholar and PubMed using a combination of relevant keywords (MeSH terms), including "Resilience," "Stroke,"

"Post-Stroke," and "Determinants" or "Factors." The inclusion criteria were quantitative or qualitative research

articles published between 2020 and 2025, available in full text in English or Indonesian, and focusing on factors

that influence resilience in adult stroke patients. A total of 11 core articles met the inclusion criteria and were

analyzed in depth. The determining factors were grouped into internal factors (such as self-efficacy, hope, self-

esteem, and coping style) and external factors (such as social support, functional status, and environment). Key

findings show that self-efficacy and social support are the most consistent predictors of resilience. Furthermore,

resilience was found to be negatively associated with psychological conditions such as post-stroke depression

and anxiety. Internal and external factors play an important role in determining post-stroke resilience. Nursing

and rehabilitation interventions should focus on increasing self-efficacy and strengthening social support

networks to improve patients' adaptive capacity.

Keywords: Psychological Resilience; Post-Stroke Patients; Self-Efficacy; Social Support.

INTRODUCTION

Stroke is a neurological emergency that ranks second as a cause of death and the leading cause of long-term

disability globally (Wang et al., 2024). In Indonesia, data from the Ministry of Health shows a continuously

increasing prevalence of stroke, reflecting a substantial disease burden on the national health system. Stroke

often results in permanent neurological deficits, including hemiparesis, cognitive dysfunction, aphasia, and

swallowing problems. These impacts are not limited to physical limitations but also trigger serious psychological

disorders.

Post-stroke patients face a long and challenging recovery process that fundamentally changes their roles and

quality of life. In the context of developmental psychology and adaptation, individuals are required to

demonstrate resilience to overcome these difficulties. Resilience is an individual's dynamic ability to adapt

positively in the face of adversity, trauma, tragedy, threats, or significant sources of stress. For stroke survivors,

resilience is key to actively participating in rehabilitation and achieving optimal functional independence

(Norvang et al., 2022).

Although resilience is very important, the reality shows that many post-stroke patients experience significant

difficulties in adjusting. Longitudinal research by (Zhou et al., 2020) consistently shows that low levels of

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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025

resilience in the acute phase are strongly correlated with an increased risk of post-stroke depression (PSD) up to

six months after discharge. Similarly, (Han et al., 2021) found that patients with low resilience tend to have

maladaptive coping styles, especially in dealing with the uncertainty of their illness. For chronic disease nursing,

the SFA emphasizes that nurses should shift from disease care to helping patients improve adaptability and

resilience (Jiang et al., 2023). This phenomenon points to an empirical gap, where expectations of post-stroke

psychological adaptation (health psychology review) are not in line with the clinical reality of patients who often

struggle with emotional and adaptive difficulties.

The scientific literature presents various findings on the factors that influence resilience, which often appear

contradictory or differ in emphasis, thus creating a theoretical gap. Some studies tend to emphasize internal

factors as the main predictors. For example, (Takil & Ökten, 2023) found a positive and strong relationship

between self-efficacy and resilience. Furthermore, (Sun et al., 2024) highlighted the mediating role of hope in

the relationship between social support and self-esteem on resilience.

On the other hand, other studies highlight external factors. Explicitly identify social support as an important

determinant of resilience, equivalent to self-efficacy (Faradisa et al., 2025). These contradictions and variations

include: (1) a focus on internal versus external variables; (2) differences in findings regarding the mediating or

moderating role of variables (e.g., the role of coping versus the role of hope); and (3) differences in populations

and clinical settings, which affect the generalizability of findings. This diversity indicates that there is no clear

consensus on the most dominant and consistent causal factors in the context of post-stroke patients.

In addition to purely psychological factors, the functional status of patients also plays a role. Liu suggested that

resilience is an independent correlate of post-stroke Quality of Life (QOL) trajectory (Liu et al., 2021).

Meanwhile, (Chen & Tung, 2021; Norvang et al., 2022) confirm a significant relationship between resilience

and Activities of Daily Living (ADL) or functional independence. We defined resilience as the ability of brain

networks to maintain their core integrative and modular properties following recurrent attacks (Dirren et al.,

2025). This indicates a complex interaction between physical recovery and psychological resilience.

This systematic review offers novelty by integrating the latest findings (2020-2025) to explicitly identify the

most consistent causal factors of low resilience, which encompass not only psychological but also functional

domains. This synthesis is urgently needed because its results can serve as a strong foundation for developing

nursing and rehabilitation interventions focused on the most important pillars of resilience, thereby improving

patients' long-term outcomes.

The objectives of this systematic review are to identify the most significant internal factors that contribute to the

psychological resilience of post-stroke patients; to identify the most significant external factors that support or

enhance their psychological resilience; and to synthesize the available scientific evidence to determine the main

and most consistent factors associated with low resilience in post-stroke patients.

Method Design

This study used the Systematic Literature Review (SLR) method based on the Preferred Reporting Items for

Systematic Reviews and Meta-Analyses (PRISMA) framework (Tricco et al., 2018). This approach was chosen

to minimize bias and provide a comprehensive synthesis of evidence regarding the factors that influence post-

stroke resilience.

Inclusion and Exclusion Criteria

The criteria used to determine the eligibility of articles to be reviewed include:

Criteria

Design

Inclusion (Included)

Exclusion (Excluded)

literature

Quantitative, qualitative, or Non-systematic

reviews,

editorials,

mixed-methods

research theses/dissertations, study protocols.

examining factors.

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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

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Adult patients (>18 years) who Pediatric stroke patients, patients with other

are stroke survivors (ischemic neurological comorbidities (other than stroke).

or hemorrhagic).

Population

Variables

Measuring

Psychological Resilience and without explicitly addressing resilience as a central

its influencing factors. variable.

or

discussing Primary focus on quality of life or depression

Published

between January Published before 2020.

Publication Year

2020 and December 2025.

Full text available in English or Only abstracts are available, or in languages other

Access & Language

Indonesian.

than English/Indonesian.

Literature Search Strategy and Data Sources

Literature searches were conducted in two main databases:

1. Google Scholar: For Indonesian-based and global literature.

2. PubMed: For evidence-based clinical and health literature.

The search strategy uses a combination of Boolean keywords. The keywords used in English and Indonesian

are:

1. (Resilience OR Psychological Adaptation) AND (Stroke OR Post-Stroke) AND (Factors OR

Determinants OR Influencing Factors)

2. (Resilience OR Psychological Resilience) AND (Stroke OR Post-Stroke) AND (Factors OR Causes OR

Determinants)

Article Selection Process (Screening)

The selection process was carried out in four stages according to the PRISMA flow:

1. Identification: All articles found from the initial search are recorded.

2. Screening: Articles are screened based on their titles and abstracts. Duplicates are removed.

3. Eligibility: Articles that meet the initial criteria are assessed for eligibility based on a full-text review.

4. Inclusion: Articles that pass full-text screening and meet all inclusion criteria (including manuscript

quality assessment) are included as core articles for synthesis.

PRISMA Flow Diagram

The following is an estimated article selection flow that will be presented in this systematic review (simulation

based on the data used):

Identification Stage

Number of Articles

Articles identified through databases (Google Scholar, ScienceDirect, PubMed) N = 261

Articles excluded before duplication check (reason: irrelevant)

N = 83

Total Articles After Title/Abstract Screening

N = 178

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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025

Articles excluded due to duplication

N = 92

Total Articles Screened

N = 86

Screening and Eligibility Stage

Number of Articles

Articles were checked for eligibility based on full text

N = 86

Articles excluded (reasons: Inappropriate population, publication year <2020, N = 62

or not a primary study)

Total Articles for Quality Assessment

N = 24

Inclusion Stage

Number of Articles

Articles excluded after manuscript quality assessment (e.g., JBI Critical N = 13

Appraisal Tool)

Total Core Articles Included in the Systematic Review

Results

N = 11

A total of 11 articles that passed the final selection were reviewed for data extraction. Table 1 summarizes the

main characteristics of each core article, including the name of the researcher, objectives, sample, instruments,

and research results, which form the basis for qualitative and quantitative synthesis in the discussion.

Table 1. Synthesis of Core Article Characteristics (N=11)

Participants

Data

Main

Research

Findings

Researcher

(Year)

Research

Title

Research

Objective

(N)

& Measurement

Instruments

Analysis

Techniq

ues

No

Sampling

Technique

1

Zhou et al. Resilience

(2020)

Examining

is associated the

217 survivors CD-RISC

of first-time (Resilience),

Logistic

Regressi

on,

Longitud significantly

inal

Initial

resilience

was

with

stroke

post- relationship

between

ischemic

stroke

Purposive

HADS

; (Depression/A

nxiety)

depression

in Chinese- resilience

-stroke and

survivors: A depression

baseline

negatively

associated

Analysis

with

post-

stroke

longitudinal

study

at 1, 3, and 6

months

depression at

1

and

3

post-

months.

discharge.

Resilience is

a protective

factor.

2

Liu et al. Resilience

(2020) is

independent Quality

correlate of Life (QOL)

Exploring

an changes in ischemic

of stroke

217 first-time CD-RISC

(Resilience),

Multilev

el Model a

Resilience is

positive

independent

predictor of

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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025

the

course from

the patients;

SS-QOL

(QOL)

improved

QOL over 6

months.

Depressive

status

mediates this

relationship.

of QOL in acute phase Purposive

patients

with

ever

ischemic

stroke

to 6 months

first- post-

discharge

and

correlation

with

its

resilience.

3

Han et al. Uncertainty

Testing the 185

stroke CD-RISC

(Resilience),

Mediatio Resilience

(2021)

in

illness role

of patients;

n

and partially

and coping resilience as Convenience

MIS

moderati mediates the

styles:

Moderating

and

a mediator

and

moderator

between

(Uncertainty),

SCS (Coping)

on

analysis

negative

effect

illness

of

mediating

uncertainty

effects

resilience in uncertainty

stroke

patients

of illness

on effective

coping styles.

High

resilience

promotes

active

and coping

styles.

coping.

4

Chen

Tung (2021) and

& Resilience

Understandi 100 inpatients RSA

the with stroke; (Resilience),

distribution Cross-

Spearma

n's

Barthel Index correlati

Total

Daily ng

Activity

Among

Patients

resilience

scores were

significantly

correlated

of variables, sectional

(ADL)

on, t-test

resilience,

After Stroke and factors

with

ADL.

that

influence

resilience

Patients

without

aphasia

had

and

daily

better ADL.

activities

(ADL).

5

Norvang et Resilience

al. (2022) and

Investigatin

181

stroke CD-RISC

(Resilience),

Regressi

on

Analysis

Resilience in

Its g

the patients;

relationship

between

the

acute

Association

With

Activities of resilience

Daily

Living

Months

Prospective

Cohort

mRS

(Functional),

SIS (ADL)

phase

independentl

y

predicts

ADL

measured at

3 baseline and

ADL

better

independenc

e 3 months

later.

After Stroke measured 3

months after

stroke.

6

Dirren et al. Determinant Test

(2025) s of brain hypothesis

network that stroke- ever

the 75

with a first- (brain

stroke function)

patients RS-FMRI

mixed-

effects

models

to

Brain

networks of

stroke

patients were

more resilient

induced

reorganizati

compare

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resilience

after stroke

on of brain (ischemic or

resilienc

e

between

patients

and

controls

and

across

time

to

virtual

functional

connectivity

enhances

network

hemorrhagic)

lesions than

those

controls,

terms of both

global

efficiency

and

modularity.

of

in

resilience to

recurrent

events,

defined

as

the ability of

brain

points

networks to

maintain

core

integrative

(global

efficiency)

and modular

(modularity

) properties

after

new

lesions

7

8

9

Heltty

Zahalim

(2023)

& Resilience

Determinin

the stroke

its relationship respondents

122

post- CD-RISC

Bivariate A significant

after stroke g

and

correlation

with

functional

independen

ce

(Resilience),

Barthel Index

Analysis

relationship

(p < 0.05)

between

resilience

and

(5-8 weeks of (Functional)

recovery);

Simple

was

found

between

resilience and

functional

independenc

e.

functional

ability

Random

in

post-stroke

patients,

particularly

independen

ce.

Takil

Ökten

(2023)

& The

Relationshi

Investigatin

110

stroke R-E

SE

Correlati Positive and

g

the patients;

(Resilience), S- on,

progressive

correlation

between

functional

independenc

p

of relationship

between

Cross-

sectional

(Self- Regressi

Efficacy), FIM on

(Functional)

Functional

Status with functional

Self-

Efficacy

and

independen

ce,

stroke

e,

self-

efficacy,

efficacy, and

resilience.

Self-efficacy

is a strong

predictor of

resilience.

Resilience

in

Stroke and

Patients

resilience in

patients.

Jiang et al. Study

(2023)

on To evaluate A total of 183 NIHSS

Chi-

square,

There are no

empirical

self-

the

adult

stroke (neurological

managemen

effectivenes

patients who status), SSBR

results

yet

t of real- s of using are about to (self

because this

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time

individualiz

ed

in

patients

based

and wearable

technology

support (smartwatch Neurology

stroke and Department

of West support)

provide China

be discharged management),

One way article is a

ANOVA study

protocol for a

from

the CD-RISC

(resilience),

MSPSS (social

randomized

controlled

wristband)

on to

trial.

stroke

resilience: a real-time,

protocol for individualiz

Hospital

Sichuan

of

patients

receiving the

Real-time

and

Individualize

d

management

Support

(RISS)

program via

smartwatch

will

a

ed

self- University

randomized

controlled

trial.

managemen

t support for Second

stroke

and Chengdu

People’s

Hospital;

Self-

patients

during

transition

from

the consecutive

sampling

hospital to

home.

demonstrate

better

self-

management

behaviors,

higher

quality

life,

of

and

lower

recurrence

and

unplanned

readmission

rates

10

Sun et al. The

Exploring

the

of mediating

210

rehabilitation

stroke

early CD-RISC

Structura Social

support

SSRS (Social Equation self-esteem

(2024)

mediating

effects

(Resilience),

l

and

hope on the effects

relationship hope on the Cross-

between relationship sectional

of patients;

Support), HHI Modelin

(Hope), SES g (SEM)

(Self-Esteem)

have

both

and

direct

indirect

effects

s

social

between

on

support and social

resilience,

mediated by

hope.

self-esteem

with

support and

self-esteem

on

psychologic

al resilience psychologic

in stroke al resilience.

patients

11

Faradisa et Self-

al. (2025) Efficacy

and Social between

Testing the 84

relationship stroke

post- SSEQ

(Self- Spearma

n's

There is a

significant

positive

relationship

between self-

efficacy and

resilience

Efficacy),

patients;

Purposive

MSPSS (Social correlati

Support), CD- on

RISC-10

Support as self-

Determinant efficacy,

s

of social

support, and

(Resilience)

Resilience

in

Post- resilience in

(r=0.512),

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Stroke

Patients

post-stroke

survivors.

and between

social

support

and

resilience

(r=0.485).

DISCUSSION

This discussion aims to interpret and critically synthesize key findings from a systematic review of factors

contributing to low psychological resilience in post-stroke patients. This review integrates quantitative data from

selected articles to identify the most consistent predictors, explain the theoretical mechanisms behind them, and

formulate evidence-based clinical implications.

Quantitative Synthesis and Discussion of Findings

The quantitative synthesis of primary studies clearly identified three clusters of interrelated determinant factors:

internal psychological variables, functional status, and environmental support as the main contributors to post-

stroke resilience. The main findings that consistently address the objectives of this study are the centrality of the

role of self-efficacy and hope, as well as the strong moderation of resilience on mental health outcomes.

Centrality of Internal Psychological Determinants: Self-Efficacy, Hope, and Self-Esteem

Comparative analysis of correlation coefficients from various studies indicates that internal psychological

variables are the strongest predictors of resilience.

A. The Dominant Role of Self-Efficacy and Functional Status A cross-sectional study by Takil & Ökten (2023)

showed a significant and strong positive correlation (P<.05) between Functional Independence (FIM) and Post-

Stroke Self-Efficacy (SSEQ), which was then positively correlated with Resilience. In this context, Self-Efficacy

functions as a cognitive bridge. Patients who are more physically independent have more mastery experiences,

which are the main source of self-efficacy development according to Bandura's Social Cognitive Theory.

Conversely, low post-stroke self-efficacy impairs the cognitive capacity to set recovery goals and maintain

rehabilitation efforts.

This empirical support is reinforced by (Faradisa et al., 2025), who found a strong correlation between self-

efficacy and resilience (a higher correlation coefficient than Social Support), confirming that subjective belief

in the ability to perform necessary activities (regardless of existing deficits) is a key prerequisite for adaptive

capacity (resilience). Stroke patients with low resilience often become trapped in a cycle of learned helplessness,

where repeated functional failures erode their core beliefs.

B. Hope as a Crucial Mediating Mechanism One of the most significant findings obtained through Structural

Equation Modeling (SEM) analysis by (Sun et al., 2024) is the role of hope as a substantial mediator.

Sun et al. (2024) found that:

1. Social Support has a direct positive effect on resilience (훽₃=0.434), but also has a significant positive

effect mediated by Hope (훽₂=0.114).

2. Self-Esteem has a direct positive effect on resilience (훽₆=0.179), which is also significantly mediated by

Hope (훽₅=0.200).

Quantitative Implications: These findings indicate that hope (defined as a which is the ability to plan a path to

achieve a goal, combination of pathway thinking, and agency thinking, which is the motivation to use that path)

functions as a psychological 'battery'. Social support or high self-esteem does not necessarily increase resilience

unless these resources are channeled through the lens of hope. If post-stroke patients cannot see a "way out" or

lack the motivation (agency) to move along that path, the benefits of external support and internal self-esteem

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will be reduced. This is why clinical interventions should focus on developing hope, not just on providing

support.

Functional Status, Neurological Complications, and Resilience

High functional dependence is an external factor that directly triggers low resilience.

A. The Relationship between ADL and Short-Term Resilience (Norvang et al., 2022) conducted a prospective

cohort study examining resilience and its relationship with Activities of Daily Living (ADL) 3 months after

stroke. They found that resilience levels were strongly and positively correlated with ADL independence.

Similarly, (Heltty & Zahalim, 2023) found a significant relationship between resilience and functional

independence.

Mechanism: Low ADL independence (e.g., low Barthel Index score) constantly reminds patients of their loss of

autonomy and control, which is a chronic stressor. Conversely, every small improvement in ADL (e.g., being

able to eat or bathe independently) serves as positive feedback that strengthens Self-Efficacy, which in turn

increases resilience.

B. Aphasia as a Barrier to Communication Resilience (Chen & Tung, 2021) specifically identified that patients

with aphasia complications had statistically significantly lower resilience scores than patients without aphasia

(p=0.0012). Aphasia, as a communication deficit, impedes three key processes of resilience building:

1. Emotional Expression: Difficulty communicating frustration and emotional needs.

2. Seeking Social Support: Difficulty asking for help or establishing meaningful social interactions.

3. Rehabilitation Participation: Limitations in understanding instructions or negotiating with therapists.

Social isolation imposed by these communication barriers drastically reduces social support buffering, which is

negatively correlated with resilience.

Resilience as a Predictor of Long-Term Health Outcomes

This review found strong longitudinal evidence placing resilience as a predictor, not merely a consequence, of

post-stroke mental health.

A. Resilience Predicts Post-Stroke Depression (PSD)

Researchers conducted a longitudinal study of 217 ischemic stroke survivors and found that resilience measured

at the acute (inpatient) phase was a significant independent predictor of the onset of Post-Stroke Depression

(PSD) at 1, 3, and 6 months after discharge (Zhou et al., 2020).

B. Resilience Modulates Quality of Life (QOL) and Coping Styles

Some researchers used a multilevel model and found that higher resilience in the acute phase predicted a steeper

and more sustained trajectory of Quality of Life (QOL) improvement up to 6 months (Liu et al., 2021). This was

reinforced by (Han et al., 2021), who showed that resilience has a moderating effect on the relationship between

uncertainty in illness and coping style. Patients with high resilience tend to use problem-oriented coping (actively

addressing deficits), while low resilience tends to lead to avoidance coping (denial, isolation).

Clinical Implications

These findings have urgent clinical implications, demanding a transition from a passive biomedical focus to a

proactive and integrated bio-psychosocial care model.

1. Resilience Screening and Early Intervention (Pre-emptive Care): Since resilience in the acute phase

predicts long-term PSD and QOL, resilience screening (using instruments such as CD-RISC or RSA)

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should be integrated into stroke patient assessment protocols. Interventions targeting resilience

enhancement should begin as early as possible (during hospitalization) to mitigate the risk of depression.

2. Priority Cognitive Interventions: Self-Efficacy and Hope: rehabilitation programs should be explicitly

designed to rebuild self-efficacy through graded mastery experience. Each physical therapy session

should be viewed as an opportunity to build self-efficacy, not just motor function. Furthermore, to foster

hope, nurses and therapists should conduct goal-setting sessions focused on pathway thinking and

provide validation of patient agency.

3. Structured Family Involvement: Given that the role of social support is mediated by hope, family

education is crucial. Families must be educated to shift from providing excessive instrumental support

(which can increase dependence) to validating emotional support and empowering informational support.

This approach aims to minimize caregiver burden, which can trigger feelings of guilt in patients.

Research Novelty/Originality

This systematic review provides significant novelty compared to previous literature reviews. Although there

have been meta-analyses identifying factors that influence resilience in general, this review stands out in several

aspects:

1. Focus on Quantitative Mediation Mechanisms: This review specifically synthesizes the latest quantitative

evidence from the SEM study by (Sun et al., 2024) that defines Expectancy not only as a correlation but

as a key mediator. This goes beyond the identification of simple correlations and provides a deeper

understanding of causality, which is highly relevant for intervention design.

2. Integration of Longitudinal Findings: By incorporating longitudinal studies (Liu et al., 2021; Zhou et al.,

2020), this review provides time-based evidence that resilience is an early predictor of PSD and QOL,

not merely a consequence of pre-existing mental conditions. The emphasis on early predictors in the

2020-2025 literature represents a critical update for the field.

3. Holistic Approach to External and Internal Factors: This review successfully integrates

neuropsychological factors (Aphasia) and functional factors (ADL) with psychological factors (Self-

Efficacy) to produce a comprehensive model of why post-stroke patients' resilience is low.

CONCLUSION AND RECOMMENDATIONS

Conclusion

Low psychological resilience in post-stroke patients is a phenomenon determined by the strong interaction

between internal psychological factors and external functional status. The main finding of this systematic review

is that deficits in self-efficacy and expectations (as mediators) are the most central predictors. Additionally, low

functional independence (ADL) and communication barriers (aphasia) significantly contribute to resilience

vulnerability. Most crucially, low resilience in the acute post-stroke phase independently predicts the onset of

post-stroke depression and a sustained decline in quality of life. Therefore, prevention and recovery efforts

should directly target strengthening patients' self-efficacy and hope.

Recommendations

1. Recommendations for clinical practice: Healthcare institutions should immediately adopt resilience

screening protocols and include psychosocial interventions targeting the enhancement of self-efficacy

through self-management training and the promotion of hope through narrative therapy and the setting

of gradual and measurable goals.

2. Recommendations for further research: Prospective intervention studies (RCTs) are needed to test the

effectiveness of rehabilitation programs specifically designed to increase resilience with a focus on

mediating variables (hope). In addition, further qualitative studies are needed to gain a deeper

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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025

understanding of the subjective experiences of patients with aphasia and how they manage social

isolation to maintain resilience.

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