Development of a Japanese version of the Self-assessment Scale of Interprofessional Competency (JASSIC)

ABSTRACT

This study aimed to develop a Japanese version of the Self-assessment Scale of Interprofessional Competency (JASSIC), which consists of six domains: Patient-/Client-/Family-/Community-Centered, Interprofessional Communication, Role Contribution, Facilitation Relationship, Reflection, and Understanding of Others. Validity of JASSIC was confirmed through a four-step process consisting of expert discussion, cognitive debriefing, feasibility, and statistical analysis. Confirmatory factor analysis (CFA) was performed by testing the correlation between the sum scores of JASSIC and the Assessment of Interprofessional Team Collaboration Scale-II(AITCS-II). First, 24 items were created through discussions among physicians, a nurse, a medical educator, and an information sociologist. Second, the items were modified by cognitive debriefing of a physician, nurse, pharmacist, occupational therapist, and social worker. Third, we provided the developed JASSIC for professionals at Hospital X (n = 139) and revised the wording and composition of the items. Finally, CFA among professionals at Hospital Y (n = 153) identified a 6-domain structure (GFI: 0.847, AGFI: 0.782, RMSEA: 0.088). Cronbach’s alpha was 0.92, and the correlation coefficient with AITCS-II was 0.72. Ongoing research into JASSIC will promote effective interprofessional collaborative practice not only in Japan but also other countries which share a similar culture and system.

KEYWORDS:

• Questionnaire design
• professional competency
• competency-based education
• self-assessment
• interprofessional relation
• interdisciplinary learning

Introduction

The current pressures on primary healthcare settings globally have revealed the challenges of the super-aging society, multimorbidity and health inequities (Samuelson et al., 2012). Such challenges cannot be contained by a single profession and require efficient interprofessional collaborative practice (IPCP). Achieving IPCP in turn requires utilization of seamless interprofessional education (IPE) from undergraduate level to continuous professional development, as presented in a framework for action by the World Health Organization (WHO) (Gilbert et al., 2010). Owing to the rigor of the curriculum and the presence of professional silos against IPE, however, healthcare education has failed to overcome dysfunctional healthcare systems (Frenk et al., 2010). In particular, while assessment of interprofessional learning is essential for the effective promotion of IPE and IPCP, no international consensus on the assessment of IPE has yet been established (Rogers et al., 2017).

In healthcare education, competency-based assessment is promoted as ideal (Gruppen et al., 2012). A set of competencies is defined as the ability to integrate that particular set of knowledge, skills, ethics, and attitudes which together characterize the successful practitioner of a profession (Ten Cate, 2005). With competency as a goal, learners or practitioners coordinate the learning process, apply the learning to practical situations, and assess performance based on expected levels (Carraccio et al., 2002). Interprofessional competencies and capabilities have been advocated in the UK, US, Canada, Australia, and Japan (Brewer & Jones, 2013; Brownie et al., 2014; Canadian Interprofessional Health Collaborative, 2010; Haruta et al., 2018; Schmitt et al., 2011), but few comprehensive tools for the broad assessment of interprofessional education competencies have yet been developed. Some tools that partially assess knowledge, skills, attitudes, and assessment process are available, including ICAR, the interprofessional collaborator assessment rubric (Curran et al., 2011); iTOSCE, the interprofessional team objective structured clinical examination (Curran et al., 2009; Simmons et al., 2011); and iTOFT, the interprofessional teamwork observation and feedback tool (Morgan et al., 2015; Thistlethwaite et al., 2016). In Japan, evaluation tools for IPE developed through linguistic and validation studies include RIPLS, the Readiness for Interprofessional Learning Scale (Oishi et al., 2017; Tamura et al., 2012); IPFS, the Inter Professional Facilitation Scale (Haruta et al., 2017); and AITCS, the Assessment of Interprofessional Team Collaboration Scale (Yamamoto & Haruta, 2019). However, none of these was developed based on interprofessional competencies in Japan. Interprofessional competency-based assessment can allow the comprehensive reflection of one’s own competency, as well as analysis of not only the differences in each profession but also the situation in each facility and local community. This can provide insights to aid the development of IPE curricula and/or community healthcare systems.

Against this background, this study aimed to develop a Japanese version of the Self-assessment Scale of Interprofessional Competency (JASSIC) as a tool for individual professionals to assess interprofessional competencies.

Methods

Development of the self-assessment scale was conducted in four steps, namely (1) expert discussion, (2) cognitive debriefing, (3) feasibility, and (4) statistical analysis. The development and validity process of JASSIC is illustrated in Figure 1.

Figure 1. Flow chart of the development and validity process.

Based on the interprofessional competency framework in Japan (Haruta et al., 2018), two authors (JH and RG) developed the items of JASSIC Ver. 1 as a prototype to measure interprofessional competencies. The Interprofessional Competency Framework in Japan includes two core domains (Patient-/Client-/Family-/Community-Centered and Interprofessional Communication) and four peripheral domains (Role Contribution, Facilitation Relationship, Reflection, and Understanding of Others) (Haruta et al., 2018). JH is the first author of a thesis aimed at promoting the development of Interprofessional Competency in Japan.

Expert discussion

Multi-professional experts who were involved in the project to develop an Interprofessional Competency Framework in Japan and who had a good understanding of the framework were asked to contribute to this study. Based on the subjective opinions of five experts, a consensus method was considered appropriate for development of the scale. Each expert evaluated JASSIC Ver. 1 by completing an e-mail survey based on a 4-point Likert scale from June to July 2019. A 4-point scale was adopted based on Lynn’s determination (Lynn, 1986) and the quantification of content validity: 1 = very valid; 2 = mostly valid; 3 = not quite valid; and 4 = not valid (Polit & Beck, 2006). Full consensus was defined as 78% or more of the responders giving a rating of 1 or 2, in accordance with a previous study (Morita et al., 2015). Additionally, we conducted web meetings using Skype ^TM to seek each expert’s views and asked each expert to suggest different wording or to propose other items. Once an expert consensus was reached, JASSIC Ver. 2 was created in August 2019 by adding items with the scoring reversed.

Cognitive debriefing

The cognitive debriefing was implemented through interviews using Zoom by one author (RG) with five health professions, including a physician, nurse, pharmacist, occupational therapist, and social worker, who worked in different healthcare facilities. The goal was to check their understanding of all items and explanations in JASSIC Ver. 2. The understandability, misinterpretability, and acceptability were then evaluated, and JASSIC ver.3 was created following further adjustment in September 2019.

Feasibility

Feasibility was evaluated by reviewing the proportion of missing values per item. For the face validity of Ver. 3, we confirmed whether the respondents were able to make interpretive decisions for each item. We analyzed individual attribute data that could indicate implicit bias in healthcare settings, such as positive evaluation in the positions of professionals (Fitzgerald, 2014; Fitzgerald & Hurst, 2017), and performed inter-item correlation (Cohen & Swerdlik, 2005). For content validity, free-text responses were grouped into positive and negative comments with categories relating to utility, comprehension, and embarrassment based on specific guiding questions.

This validity process was implemented via research collaborators through the distribution of JASSIC Ver. 3. to all healthcare professionals in Hospital X, a community hospital, by the head of nursing. Study participants who did not consent to participate in the study or who did not answer every item were excluded. The self-assessment scale was collected in November 2019, and the results were used to develop JASSIC Ver. 4.

Statistical analysis

The purpose of the analyses was to evaluate the structural validity, internal consistency, and hypothesis testing for construct validity of JASSIC Ver. 3 and Ver. 4. The items of JASSIC Ver. 3 were revised or erased based on the correlation matrix for correlation coefficients under 0.3 (Tabachnick & Fidell, 2014). JASSIC Ver. 4. was distributed to all healthcare professionals in Hospital Y, a community hospital, from January to February 2020 by the hospital administration. For descriptive statistical analysis, scores for JASSIC Ver. 4 were summarized as mean, standard deviation, and percentage of floor and ceiling scores after excluding missing values. Through this process, floor/ceiling effects greater than 50% were eliminated. For structural validity, exploratory factor analysis (EFA) was used due to insufficient analysis of the structure of JASSIC Ver. 4. In EFA, Promax rotation was performed in conjunction with the maximum likelihood method (Taherdoost et al., 2014). Factor selection was based on the Scree test (Cattell, 1966) and Kaiser Eigen values >1.0 (Kaiser, 1960) as a reference. In this process, we estimated the number of factor loadings, and eliminated any items exhibiting factor loadings inferior to 0.4 for several dimensions (Maskey et al., 2018). Based on the JASSIC final version with the remaining items, confirmatory factor analysis (CFA) was implemented to explore structural validity using Structural Equation Modeling (SEM)(Simon et al., 2010). The fit of the models to the data was determined by exploring the goodness-of-fit index (GFI), adjusted GFI (AGFI) and the root-mean-square error of approximation (RMSEA) on the hypothesis that the factor structure matches the six domains of the interprofessional competency framework in Japan (West et al., 1995). Internal consistency in JASSEC final version was assessed using Cronbach’s alpha (Nunnally & Bernstein, 1994). Since no other Japanese tools for self-assessment of interprofessional competency are available, hypotheses testing for construct validity were checked by comparison with another measurement instrument (convergent validity) and between subgroups (known-group validity)(Davidson, 2014). For convergent validity, the correlation between the total score of the Assessment of Interprofessional Team Collaboration Scale-II (AITCS-II)(Orchard et al., 2018), whose Japanese version was validated in a similar way to the present questionnaire (Yamamoto & Haruta, 2019), and the total score of JASSIC final version was examined using Pearson’s correlation coefficient. For known-group validity, the total scores for JASSIC final version between nurses and non-nurses were analyzed with an unpaired t-test based on a previous study in Japan, which showed that the AITCS score for nurses is higher than that for other professionals (Haruta et al., 2019). Analyses were performed using IBM SPSS software (version 26) and Amos 25 Graphics. This study was approved by the Ethics Committee of the Faculty of Medicine of the University of Tsukuba (No. 1418). All participants were volunteers who had checked a box on the questionnaire indicating their intention to participate.

Results

Expert discussion

JH and RG created JASSIC Ver. 1 with 26 items, consisting of 5 items for each of the core domains of Patient-/Client-/Family-/Community-Centered and Interprofessional Communication, and 4 items for each of the peripheral domains of Role Contribution, Facilitation Relationship, Reflection, and Understanding of Others. Five experts, consisting of a male family physician with a 16-year career, a female family physician with a 16-year career, a female nurse with a 19-year career, a medical educator with a 10-year career, and an information sociologist with a 11-year career, all of whom were involved in the development of interprofessional competency in Japan, evaluated JASSIC Ver. 1 using the sheet to check the 4-point scale and describe the free text of each item via e-mail. The experts initially failed to reach full consensus for all items. By checking all items in two web meetings and three e-mail interactions, a 24-item JASSIC Ver 2. was created, which included four reverse scoring items.

Cognitive debriefing

The five health professionals who performed the cognitive debriefing, namely a physician, nurse, pharmacist, occupational therapist, and social worker, are characterized in Table 1. Each professional underwent a debriefing that took between 20 and 35 minutes. The authors discussed the findings of the debriefing and determined that three items should be revised using different wordings, and that one item should be rearranged to make it easier to answer. The resulting document was considered JASSIC Ver. 3.

Table 1. Attributes of the five healthcare professionals involved in cognitive debriefing from June to August 2019.

Age (years)	36	34	33	32	40
Gender	F ^a	M ^a	M ^a	F ^a	F ^a
Profession	Family Physician	Occupational Therapist	Pharmacist	Social Worker	Visiting nurse
Length of career (years)	12	12	7	10	18
Working facility	Clinic and university hospital	Home visiting rehabilitation in community hospital	Drug store	Social welfare council	Home nursing station
Duration of debriefing (min)	30	25	30	35	20
a. F: Female, M: Male

Feasibility

JASSIC Ver. 3 was distributed to 149 healthcare professionals at Hospital X. Of these 6 did not provide consent and 4 were excluded due to missing values, leaving the data of 139 participants for analysis. Baseline characteristics of the participants in Hospital X are shown in Table 2. For face and content validity, we found that participants might misinterpret the reverse scoring items, given that three of four reverse scoring items (No.15, 19, 20) had inter-item correlations of 0.3 or less for more than half of responses, in absolute values. (Supplemental material.1)

Table 2. Attributes of healthcare professional participants using JASSIC Ver. 3 in Hospital X in November 2019 and JASSIC Ver 4. in Hospital Y from January to February, 2020.

			Hospital X (n = 139)		Hospital Y (n = 153)
Age (years) ^a		39.1 ± 11.0		41.5 ± 11.1
Gender ^b	F ^c	115 (82.7)		115 (75.2)
	N/A ^d	2 (1.4)		0 (0)
Profession ^b	Doctor	7 (5.0)		7 (4.6)
	Nurse	88 (63.3)		98 (64.1)
	Pharmacist	7 (5.0)		6 (3.9)
	Rehabilitation therapist	5 (3.6)		14 (9.2)
	Radiologist	6 (4.3)		7 (4.6)
	Clinical technologist	8 (5.8)		11 (7.2)
	Social worker	2 (1.4)		0 (0)
	Other	11 (7.9)		10 (6.5)
	N/A ^d	5 (3.6)		0 (0)
Years of experience as professionals (years) ^a		14.3 ± 10.2		17.4 ± 10.6
Years of experience at their institution (years) ^a		7.2 ± 7.6		9.5 ± 7.9
Total score of JASSIC final version ^a		N/A^d		62.8 ± 9.9
Total score of the AITCS-II^a		72.1 ± 17.3		72.4 ± 15.5
a. Average ± Standard deviation	b. Number (%)	c. F: Female		d. Not applicable

Statistical analysis

First, the data of JASSIC Ver. 3 were analyzed. Excluding the four reverse items, the inter-item Spearman’s rank correlations were all significant and ranged from 0.29 to 0.71 (r > 0.2, p < .05). Further, Pearson’s correlation coefficient between the total score of JASSIC Ver. 3 and AITCS-II was 0.67 (p < .001). Based on these findings, the authors developed JASSIC Ver. 4, which changed the reverse items to regular ones because three of the reversed items of Ver. 3 scored 0.3 or less on the correlation matrix for correlation coefficients.

Second, JASSIC Ver. 4 was distributed to 165 healthcare professionals at Hospital Y, of whom 8 did not provide consent and 4 were excluded due to missing values, leaving 153 participants for analysis. Baseline characteristics of the participants in Hospital Y are shown in Table 2. No floor and ceiling effect was seen. In EFA with six fixed factors based on the domains of interprofessional competency in Japan, six items were excluded, since factor loadings were less than 0.4 for several dimensions. The Kaiser-Meyer-Olkin (KMO) value was 0.875 and Bartlett’s test for sphericity was significant (χ2 = 2144.543, df =153, p < .001). EFA fixed 6 factors showed a cumulative contribution rate of 82.3%, while the individual contribution rates in the order of interprofessional competency in Japan were 44%, 3.6%, 7.7%, 6.6%, 3.7%, and 16%, respectively. Factor loadings after rotation are shown in Table 3. JASSIC final version was created following this process. Mean and standard deviation of the total score of JASSIC final version were 62.8 ± 9.9 (full 90) in Table 2. All items of JASSIC final version exhibited an inter-item correlation ranging from 0.17 to 0.82, except for 0.15 (correlation between item 11 and item 23) (Supplemental material. 2), and also showed significant correlation by Spearman’s correlation coefficient (p < .05). CFA of JASSIC final version indicated a 6-domain structure, with 0.847 for GFI, 0.782 for AGFI, and 0.088 for RMSEA (90% confidence interval [CI]: 0.073–0.102) (Figure 2). Cronbach’s alpha was 0.92 for all items, and 0.95, 0.88,0.87,0.84,0.90, and 0.81 for each order factor (Table 3). Pearson’s correlation coefficient between the total score of AITCS-II and JASSIC final version was 0.69 (p < .001). For known-group validation using JASSIC final version, the mean and standard deviation of the total scores of 98 nurses and 55 non-nurses were 64.9 ± 9.6 and 59.3 ± 9.6, respectively, which were significantly different (p = .001).

Table 3. Exploratory factor analysis of JASSIC final version* with 18 items in Hospital Y from January to February 2020.

Item	Factor loading
	Factor 1^a	Factor 2^b	Factor 3 ^c	Factor 4^d	Factor 5^e	Factor 6 ^f
2. I regularly convey the concept of values and concerns of patients and clients as well as their families to multi-healthcare professionals.	0.91	0.13	−0.12	−0.09	0.03	0.03
3. I discuss the goals of therapeutic interventions and healthcare focused on patients, clients, and their families with multi-healthcare professionals.	1.01	−0.13	0.03	0.00	−0.04	0.05
4. I share what I have communicated to patients, clients, and their families with multi-healthcare professionals involved in therapeutic interventions and healthcare.	0.97	−0.06	0.01	0.09	−0.02	−0.06
5. I regularly convey information that I have come to acquire to multi-healthcare professionals.	0.33	0.47	0.12	−0.04	0.14	−0.10
7. When I answer and ask questions with patients, clients, and their families, I pay due respect to multi-healthcare professionals for their roles and opinions (including non-verbal communication).	−0.01	0.90	−0.05	0.04	−0.03	0.06
8. I explain my own views and opinions in terms clearly understandable to other professionals.	0.03	0.75	0.13	0.04	0.01	−0.06
9. I convey my general knowledge and concept of values to other professionals.	0.25	0.25	0.42	0.03	−0.15	0.12
11. I fulfill my responsibility among multi-healthcare professionals in the care of patients and clients.	0.04	0.01	0.82	0.04	0.08	−0.13
12. I fill the role and functions requested of me by multi-healthcare professionals.	−0.07	−0.01	1.04	−0.06	−0.02	0.04
14. I have an equal relationship with multi-healthcare professionals.	0.00	0.19	−0.19	0.77	0.09	−0.07
15. I feel I am growing together with multi-healthcare professionals.	0.05	−0.18	0.08	0.84	0.07	−0.01
16. I am careful about not causing situations of interpersonal conflict with multi-healthcare professionals.	−0.06	0.10	0.09	0.72	−0.15	0.11
17. I understand the role and functions expected of me by multi-healthcare professionals.	−0.02	−0.12	0.11	0.16	0.46	0.28
19. I understand the role and functions I am to perform at the facility where I serve as a staff member.	−0.02	0.01	−0.03	0.00	1.06	−0.07
20. I understand that my actions may affect other professionals.	0.00	0.01	0.06	0.04	0.58	0.27
21. I understand the role and functions of other professionals at the facility where I serve as a staff member.	0.04	0.08	0.01	−0.10	0.39	0.48
22. I understand the concept of values that other professionals tend to have.	−0.02	−0.04	0.03	−0.01	−0.03	0.79
23. I understand the work environment in which other professionals operate.	0.02	0.04	−0.11	0.03	−0.11	0.93
Eigenvalue	8.06	0.65	1.39	1.19	0.67	2.87
Percent variance explained	44.8	3.6	7.7	6.6	3.7	15.9
Cronbach’s alpha = 0.924 (All items)	0.95	0.88	0.87	0.84	0.9	0.81

*Factors were divided into 6 domains based on the Interprofessional Competency in Japan.

a: Patient-/Client-/Family-/Community-Centered, b: Interprofessional Communication, c: Role Contribution, d: Facilitation Relationship, e: Reflection, f: Understanding for Others

Figure 2. Confirmatory factor analysis of JASSIC final version with 18 items in Hospital Y from January to February 2020.

Discussion

We developed a Japanese version of the Self-assessment Scale of Interprofessional Competency (JASSIC) in a robust process which involved four steps. The JASSIC is a tool to assess one’s own interprofessional competencies according to the interprofessional competency framework in Japan. This assessment tool provides a visualization of the current situation for each profession and organization and can facilitate reflection, particularly in the primary care setting, in which IPCP is required.

Since the interprofessional competency framework consists of six domains (Haruta et al., 2018), it was hypothesized that JASSIC would be constituted into six factors based on the competencies. However, in psychological statistics, the reversed items affected the participants’ perceptions (Baumgartner & Steenkamp, 2001). The inclusion of reversed items may eliminate the acquiescence bias (Watson, 1992). However, it was reported that inclusion of reversed items may cause confusion among the respondents (Sonderen et al., 2013) and distort the factor structure (Savalei & Falk, 2014; Zhang et al., 2016). In this study, we had to exclude the reversed items because they were either weakly correlated with the other items or had low factor loading. This suggests that an assessment consisting of non-reversed items provides a more robust factor structure for healthcare professions.

In the EFA of JASSIC Ver.4, two factors did not meet Kaiser Eigen values >1.0 (Kaiser, 1960). However, in general, Kaiser criteria overestimate the correct number of factors by 66% (Linn, 1968) and tend to substantially overestimate and/or underestimate the number of factors (Zwick & Velicer, 1986). Some studies have mentioned that Kaiser criteria are among the least accurate methods for selection of factor retention (Ledesma & Valero-Mora, 2007; Velicer & Jackson, 1990). Thus, instead of strictly adhering to the Kaiser criteria, the factors were theoretically selected by interprofessional competency domains.

The JASSIC final version was validated using psychometric evaluation methods (Gärtner et al., 2018). A missing data rate of 4.8% suggests that the feasibility of the scale is acceptable. Generally, while the cutoff values for acceptability were taken to be GFI, AGFI > 0.90, GFI and AGFI values in this study were 0.88 and 0.78, respectively. This might have been affected by the sample size, on which GFI and AGFI are known to depend (Mulaik et al., 1989). The RMSEA value of 0.088 in this sample is close to acceptable (Fabrigar et al., 1999). Internal consistency across the 18 items in JASSIC final version was assessed using Cronbach’s alpha, with an alpha of ≥ 0.70 indicating adequate reliability. Regarding hypothesis testing for construct validity, the correlation coefficient between the total score of AITCS-II and JASSIC final Ver. was ≥0.50, which supports convergent validity. Known-group validity demonstrated the expected difference among relationships between nurses and non-nurses (Haruta et al., 2019). Overall, these validated steps warrant the value of JASSIC final version as a self-assessment tool.

There are three strengths in this study. First, we developed a competency-based assessment tool structured into six domains. A factor structure divided into six domains is robust, as it allows for comparisons on a domain-by-domain basis. Second, JASSIC may be continuously utilized from the student to professional level by clarifying milestones in this process, given that interprofessional competency in Japan develops seamlessly from undergraduate level to continuous professional development. Third, JASSIC is based on an interprofessional competency framework developed in Japan. IPE and IPCP are influenced by culture and systems. These findings are also meaningful for other countries in Asia, where IPE and IPCP are developing rapidly, and which have relatively similar cultures to Japan. Moreover, JASSIC allows comparison of differences in interprofessional competencies among other countries, which can provide insights into the development process of competency-based assessment tools.

Several limitations of our study also warrant mention. The composition of items included no revised items. Since the JASSIC is an interprofessional competencies assessment tool with self-report measures, it may include acquiescence and social desirability. As other limitations, small sample size for CFA and how the goodness-of-fit values were barely adequate hence a further study with a larger sample would be needed. However, while it is important that this assessment tool have valid contents and a robust structure, we expect it will encourage leaners and practitioners to reflect on their progress by making them aware of learning points in each domain. The study participants were all healthcare professionals in community hospitals; future research should be applied to other facilities.

Allowing for these limitations, the results are significant because few relatively robust interprofessional competency-based assessment tools have been reported internationally. In addition, we believe that the JASSIC – developed under the concept of competency-based education – can be applied in principle not only to other institutions and communities, but also to learners and practitioners. It is expected that self-assessment by each profession, especially in the field of primary care, where effective IPCP is required, will encourage reflection by individuals, organizations, and communities, and thereby build better IPCP in the field. Future investigation into correlations among patient outcomes and JASSIC and national surveys using JASSIC will be of interest. Additionally, JASSIC might be helpful for other Asian countries which can be identified as within the Confucian Asia cluster, which includes Japan (Gupta et al., 2002). Further, we believe that it will be internationally meaningful to describe how JASSIC can be used in the primary care setting.

Conclusion

We developed and validated a Japanese version of the Self-assessment Scale of Interprofessional Competency (JASSIC). JASSIC research will promote the development of effective IPE and IPCP not only in Japan but also other Asian countries.

Declaration of interests

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this article

Ethical approval

Ethics Committee of the Faculty of Medicine, University of Tsukuba (No. 1418).

Acknowledgments

The authors thank Dr. Youhei Mori, Dr. Ai Oishi, Dr. Kazue Yoshida, Dr. Michiko Goto, and Dr. Kenji Yoshimi for their expert discussion, and Dr. Yu Yamamoto, Mr. Ryohei Gokan, Ms. Yuka Shindo, Mr. Tomotsugu Yamakawa, and Ms. Kaori Naganuma for cognitive debriefing. We are very grateful to the professional healthcare participants of Kamisu-Saiseikai Hospital and Takahagi Kyodo Hospital.

Data availability statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ethical considerations

Supplementary materials

Supplemental data for this article can be accessed on the publisher’s website.

Additional information

Funding

This study was supported by the Interprofessional Collaboration and Community-based Integrated Care Committee of the Japan Primary Care Association. This work was supported by JSPS KAKENHI Grant-in-Aid for Young Scientists (B) Grant Number JP19K19377.