Validating the caregiver self‐efficacy in contribution to self‐care scale Thai version for stroke: A psychometric evaluation

Abstract Aim To test the validity and reliability of the Caregiver Self‐Efficacy in Contribution to Self‐Care Scale Thai Version (CC‐Self Efficacy Scale (Thai)) for Stroke. Design A cross‐sectional study was undertaken from September to December 2022. Methods Four hundred thirty‐four caregivers of people with stroke were selected from the registry of stroke patients in primary care units or hospitals following inclusion criteria. The research assistants collected information when the caregiver took a patient for a doctor's appointment or visited the patient's and caregiver's home. Results The 434 caregivers had a mean age of 48 years, female 77.67%, 51.97% child or grandchild of patients, and 72.85% living with the patient. Ten items of the CC‐Self Efficacy Scale (Thai) were normally distributed and appropriate for exploratory factor analysis (EFA). EFA suggested three‐factor model. The confirmatory factor analysis (CFA) of the three‐factor model was an unfit model, with the root mean square error of approximation (RMSEA) = 0.09. We regrouped items based on content to create six‐factor model. CFA supported the six‐factor model of CC‐Self Efficacy Scale (Thai) questionnaire with the reliability judged by McDonald's omega being 0.87. The 434 sample size was enough for EFA and CFA. The CC‐Self Efficacy Scale (Thai) with the six‐factor model is appropriate for evaluating the caregiver confidence of people with stroke.

eight countries (Lebanon, Colombia, South Korea, United States, Turkey, Denmark, Sweden and South Africa) found an estimated cost of about 1809.50-325,108.80US, of which 86.20% were medical expenses, and 13.80% were productivity loss and caregiver costs (Rochmah et al., 2021).Furthermore, healthcare costs for strokes in the community were higher than those in hospitals (Tyagi et al., 2018).
Advancements in medical management systems have reduced hospital stay time, and decreased stroke deaths, leading to more survivors with residual physical disabilities (Tyagi et al., 2018).
Healthcare costs decreased for inpatient and emergency departments, while primary care service costs increased (Tyagi et al., 2018;Zhang et al., 2019).Despite advances in medical care, stroke continues to be a leading cause of death and longterm disability globally.These outcomes are connected to several major stroke risk factors and lack of timely access to stroke units (Caprio & Sorond, 2019;Suwanwela, 2014;Venketasubramanian et al., 2017;Nambiar et al., 2022).
Risk factor control strategies are essential in consistently reducing the mortality rate from stroke and preventing recurrent stroke (Caprio & Sorond, 2019).Stroke caregivers are essential to the long-term care of those suffering from recurrent stroke and modify patients' risk behaviours (Caprio & Sorond, 2019).Most caregivers are family members (e.g.spouses, children and siblings) or close friends, with caregivers and patients influencing each other (Lobo et al., 2021;Tyagi et al., 2018;Vellone et al., 2021).Caring for post-stroke at home is complicated and varies according to the pathology of the disease, symptoms of post-stroke, and the stroke patient's need for care in terms of physical, mental, emotional and social aspects (Hekmatpou et al., 2019;Lobo et al., 2021).Caregiver confidence is one of the critical factors influencing caregiver participation in the care of stroke patients (Vellone et al., 2021).Therefore, a tool is needed to measure the confidence of caregivers caring for patients in the community.
De Maria et al. (2021) developed and tested the Caregiver Self-Efficacy in Contribution to Patient Self-Care (CSE-CSC) Scale.This instrument tested the validity and reliability of caregiver selfefficacy in multiple chronic conditions (MCCs), with the root mean square error of approximation (RMSEA) = 0.07 and the global reliability index for multidimensional = 0.92.The CDE-CSC was found suitable for use as a caregiver self-efficacy test of MCCs in seven centrals and southern Italy regions but has not been used by caregivers for single chronic conditions (De Maria et al., 2021).The original CSE-CSC scale (English) has been translated into CC-Self Efficacy Scale (Thai) (https://self-care-measu res.com/proje ct/ careg iver-contr ibuti on-self-effic acy-scale -thai/).One of the authors was the principal translator using a standard forward and backward translation procedure by seven experts (two forward translators, three synthesizers and two back translators).This instrument has been judged valid by nine experts with kappa coefficients of 1.00.
Caregivers play a statistically significant role in helping patients with self-care (Vellone et al., 2021).Caregiver self-efficacy refers to an individual's belief in their ability to provide competent effective care for unwell patients.It is a critical determinant of the quality of caregiver-patient dyads provided after strokes (Boonsin et al., 2021;Honado et al., 2023;Wang et al, 2021).Hence, it is necessary to evaluate caregivers' self-efficacy in various settings and for particular chronic illnesses.Stroke provides a key example of chronic illness where disability is often involved and care needed.The instrument's CC-Self Efficacy Scale (Thai) has been developed, and here, we examine the validity and reliability of this scale measure.The results will get the appropriate instrument for healthcare professionals to further explore the caregiver's selfefficacy in contributing to self-care in the post-stroke, leading to better health outcomes for both the caregiver and the stroke survivor.

| Study and sample
The cross-sectional study was conducted from September to December 2022, including 434 caregivers of people with stroke (237 in Songkhla, 81 in Trang, 47 in Nakon Si Thammarat, 40 in Suratthani and 29 in Phatthalung).We focused on the caregivers of people with stroke whom a medical practitioner had diagnosed with the codes I60-I64 in the International Statistical Classification of Diseases and Related Health Problems (ICD-10) from the registry of stroke patients in primary care units or hospitals.Sixteen nurses or public health officers in the primary care unit and hospital selected the 434 caregivers with the following inclusion criteria: over 18 years old, primary caregivers who continuously care for stroke patients in the family, and communicating and reading the Thai language.
They directly contacted the caregiver to ensure they understood the objectives, and the participants signed informed consent forms before data were collected.Questionnaires were collected when the caregiver took a patient to a doctor's appointment or the stroke patient's home visit.

| Measures and data analysis
The questionnaire consisted of two parts.Part 1: Caregiver demographics included sex, age, education, marital status, employment status, family income, relative with the patient, living with, secondary caregiver, year of caregiver and underlying disease.
We performed a confirmatory analysis of the CC-Self Efficacy Scale (Thai) in caregivers of people with stroke in Thailand.
Factor analysis investigated the validity of questions using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA).EFA is used to analyse data to extract the new factor structure and examine the interrelationships among variables (Kim et al., 2016;Williams et al., 2010).There are three steps for the EFA (Shrestha, 2021).
Bartlett's test of sphericity was used to test the null hypothesis for the identity matrix, as follows; H o : The variables are uncorrelated.
H 1 : The variables are correlated.
A statistically significant level of Bartlett's test (p < 0.05) indicated that factor analysis is suitable (Hair et al., 2010).
Step 2: Assess the communality of the variables: Principal components analysis (PCA) is for extracting factors.Communalities are the amount of original variance shared within each variable extracted from a common factor in the analysis, ranging from 0 to 1.If the communality is close to 1, most of the information was extracted, and if it is more than 0.50 extra information is needed to explain (Hair et al., 2010).
Step 3: Choosing the number of factors: Parallel analysis is used for deciding factors to extract or retain, considering plots where the eigenvalues of the FA actual data are higher than plots of the FA simulated data line on the Scree plot (Woods & Edwards, 2007;Revelle, 2020).Factor loadings consider the correlation between the original variable and the factors, ranging from −1 to 1.The sum of squared loadings (SS loading) is used to determine the value of a particular factor, considering the SS loading more statistically significant than 1.Then, the varimax-rotation component analysis is used to extract the factor loadings to ensure that they are uncorrelated or independent of each other (Revelle, 2020).
Finally, we used the CFA to verify a set's factor structure from EFA compared with the new factor structure by the researcher.The model fit used criteria by a chi-squared test, model significance (p < 0.001 considering reject; comparative fit index (CFI), value above 0.90 indicating good fit; and the root mean square error of approximation (RMSEA), values below 0.08 indicating good fit (Hox, 2021).
We performed all statistical analyses using R version 4.1.3(R Core Team, 2022) with the psych package, lavaan package and ltm package.

| E THI C S S TATEMENT
This study was part of a study to test the validity and reliability of the scales measure self-care for individuals, and caregiver contribution to self-care in persons with stroke.This was approved by the Human Research Ethics Committee, Walailak University (approval no.WUEC-22-232-01); the standards specified in the Declaration of Helsinki were used in this study.All participants were informed of the study's rationale and purpose and signed informed consent at the beginning of the study.

| RE SULTS
Table 1 shows the sample consisted of 434 caregivers caring for people with stroke, mean age 48.28 + 13.03 years, primarily female, and had achieved an educational level bachelor's degree or higher degree.Most caregivers lived with the patients and were the patients' children or grandchildren.They had been providing care for an average of 7.35 years and had a secondary caregiver for patients.

Most of the caregivers did not have underlying diseases.
Table 2 shows the descriptive statistics for individual items of the CC-Self Efficacy Scale (Thai).All the CC-Self Efficacy Scale (Thai) items were normally distributed.

| Test for sampling adequacy and test assumption of factor analysis
The KMO measure of sampling adequacy was 0.93 and the Measures of Sampling Adequacy (MSA) for individual variables were 0.89-0.95,meaning the sampling is appropriate for exploratory factor analysis (EFA).Bartlett's test of sphericity was less than 0.001, meaning we can accept the hypothesis that the 10 variables were related to each other for EFA (Table 3).

| Assess the communality of the variables
PCA was used in this test.Values of extracted communalities were 0.59-0.69,meaning that more of the variance of individual items was explained (Table 4).

| Choosing the number of factors
Parallel analysis showed the relationship between items' variance and the number of retained items with scree plots.Figure 1 shows three factors considered with a line of FA actual data above FA simulated data, meaning that these three factors were retained.Each scale's eigenvalues were factor 1 = 5.97, factor 2 = 0.46 and factor 3 = 0.23.The sums of squared loading of factor 1, factor 2 and factor 3 were 2.92, 2.27 and 1.82, respectively.Factor 1 consisted of four items (items 7, 8, 9 and 10), and factor loading was 0.69-0.74.Factor 2 consisted of three items (items 1, 2 and 3), and factor loading was 0.63-0.83.Factor 3 consisted of three items (items 4, 5 and 6), and factor loading was 0.55-0.67.
We rematched items assessing items and the content to a sixfactor model: item 1, item 2 and item 3, item 4 and item 5, item   F I G U R E 2 Result of the confirmatory factor analysis (CFA) for the CC-Self Efficacy Scale (Thai).

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and item 7, item 8 and item 9, and item 10.
Figure 2 shows the factor structure's similarity and renamed six factors: F1 = care for stable; F2 = persist in following the treatment; F3 = monitoring the condition; F4 = recognize changes; F5 = persist in finding a remedy; F6 = evaluate to relieve symptoms.The six-factor model fit indices were as follows: chi-square = 65.53 (df = 22, p < 0.001), CFI = 0.99, TLI = 0.97, SRMR = 0.02, RMSEA = 0.06 (90% CI = 0.05-0.09)(Figure 2).Tucker-Lewis Index (TLI) was 0.99, meaning that the model was consistent with the empirical data.The reliability index for the multidimensional scale as McDonald's omega was 0.87.5 | DISCUSS IONThis study aimed to test the psychometric characteristics of the CC-Self Efficacy Scale (Thai) questionnaire to measure caregiver F I G U R E 1 Parallel analysis scree plots, parallel analysis suggests three factors (R Output).
Demographic data of 434 caregivers caring for people with stroke.
a Mean ± Standard deviation.

caregiver self-efficacy in contribution to self-care scale* Initial Extraction communalities
Descriptive statistics of individual items in the CC-Self Efficacy Scale (Thai).Kaiser-Meyer-Olkin measure of sampling adequacy and Bartlett's test of sphericity.
TA B L E 3 TA B L E 4 Communalities of variables in the Thai CSE-CSC.Items of