Introduction

Physical examination and health assessment (PEHA) is an introductory course in nursing education. In this class, students are expected to learn essential assessment skills and apply the nursing process systematically to provide patient-focused care. Assessment is the first step of the nursing process. All nursing care activities are designed to address the nursing problem identified after data have been collected and assessed. Thus, improving the assessment skills of nursing students is essential to strengthening their ability to solve clinical problems (American Nurses Association [ANA], 2021). Assessments are a systematic and dynamic process in which nurses collect and analyze patient-related information for problem identification or diagnosis during interactions with patients, their significant others, and caregivers (ANA, 2021). Therefore, assessments require more than systematic physical examination skills and should encompass the four components of data collection, data analysis and interpretation, diagnosis, and data recording (Chen et al., 2013; Weber & Kelley, 2021). When properly performed, assessments should identify patient health problems accurately and analyze their influential factors from a holistic perspective (ANA, 2021).

PEHA-related research has focused primarily on instructor lecture content (Chen et al., 2013), the clinical implementation of physical examination skills (Egilsdottir et al., 2019), learning PEHA using flipped classrooms (Choi et al., 2021), and facilitators of and barriers to the application of PEHA skills (Maniago et al., 2021; M. W. Tan et al., 2021). However, to perform accurate assessments and make appropriate nursing diagnoses, students must learn how to collect both subjective and objective data using effective communication, observation, physical examination, and (most importantly) thinking skills.

Although students are taught physical examination skills at school, they generally do not possess sufficient confidence or knowledge to provide care (Maniago et al., 2021) during their clinical clerkship (Egilsdottir et al., 2019) and in future clinical practice (Birks et al., 2013). The reasons for this difficulty in applying physical examination skills include (a) the lecture-heavy nature of conventional teaching (Birks et al., 2013; Chen et al., 2013), (b) the gap between teaching content and clinical practice (Birks et al., 2013; Egilsdottir et al., 2019), (c) the biomedical-model orientation of related curricula (Byermoen et al., 2021) with an emphasis through physical examination techniques on systematic diseases in details (K. Tan et al., 2018), and (d) the lack of holistic training on interviewing skills for subject data collection (Chen et al., 2013; Maniago et al., 2021). Faculty who teach PEHA courses in Taiwan also encounter these issues. The curriculum must be designed based on the four assessment components using different teaching strategies to bridge the gap and facilitate the application of these skills by students to resolve patient problems in future clinical practice. The PEHA curriculum is content heavy. Incorporating a self-directed learning (SDL) model in classroom training may encourage students to learn independently to acquire the necessary skills during clinical practice.

SDL is an increasingly important trend in university education and a foundational concept underlying adult education. The following highly cited definition of SDL comes from Knowles (1975, p. 14): SDL is “a process in which individuals take the initiative without the help of others in diagnosing their learning needs, formulating goals, identifying human and material resources, and evaluating learning outcomes.” This definition accentuates the self-direction ability of individuals to self-manage their learning tasks. Garrison (1997) stated that the definition of SDL should include the cognitive and motivational aspects of learning processes, namely, self-management, self-monitoring, and motivation. Self-management reflects the ability to manage learning activities by controlling learning situations and tasks. Self-monitoring reflects the cognitive responsibility of individuals to achieve meaningful learning by monitoring their learning processes. Through applied cognitive and metacognitive strategies, individuals can plan and think about their learning situations in the context of their learning tasks and goals. Effective self-monitoring relates to internal and external feedback. Motivation reflects the process used to decide whether to participate (i.e., entering motivation) and determine the level of effort to invest in performing and completing a task (i.e., task motivation). During the learning process, students should be motivated to learn and decide what should be learned and how to learn it. They should be willing to continue their learning, take responsibility for their learning, monitor their learning process, and achieve meaningful and valuable learning outcomes using efficient self-managing strategies (Garrison, 1997).

Zimmerman and Schunk (2001) also considered learning to be the product of a learner's initiative to engage in learning at the explicit or implicit level, as learning is achieved by proactive initiative rather than passively. When students engage in learning based on goals, apply effective learning strategies, monitor their learning outcomes, and adjust their learning demands, they evolve into self-directed learners. Through this process, the interactions between students' cognition, motivation, behavior, and the environment influence learning outcomes (Zimmerman & Schunk, 2001). In addition, the concept of SDL may be applied to self-adjustment in learning to help students control uncertainty in the learning environment and develop SDL abilities to attain learning goals (Bell, 2017).

The SDL framework has been used in diverse populations to explore its effect on student learning outcomes. For example, Li et al. (2021) found a supportive SDL environment to be helpful to junior high students in learning a foreign language by increasing their motivation and autonomy for extensive reading ability. In a sample of postgraduate vocational and adult education students, Chukwuedo et al. (2021) found that an SDL intervention improved study engagement and lifelong learning tendencies significantly. In addition, Noh and Kim (2019) reported the positive effects of an SDL program on SDL competency and learning satisfaction among nursing students in clinical practice. In a study of a self-directed measures intervention in a PEHA course, Shin et al. (2017) introduced a 3-hour self-directed course, finding the experimental group scored significantly higher on respiratory and musculoskeletal systems knowledge, significantly lower on academic confidence, and similarly on learning satisfaction compared with the control group. On the basis of the above observation, the aim of this study was to evaluate the effect of an SDL-framed PEHA course on student learning outcomes.

Methods Design and Participants

A quasi-experimental pretest–posttest design was used, and students were recruited from a baccalaureate program at a university of science and technology in central Taiwan using convenience sampling. The inclusion criteria were second-year undergraduate nursing students participating in the physical assessment course and agreement to participate. Two classes were randomly selected and assigned as the experimental and control groups, respectively. Eighty-nine students agreed to participate, with 48 in the experimental group and 41 in the control group, and 45 and 40, respectively, completing the entire study.

Intervention

Both groups had the same teaching objectives and used the same textbook. The control group received traditional teaching methods, including lectures, demonstrations and practice, and skill tests. The experimental group additionally took the SDL program, developed based on Garrison's (1997) SDL model. This program addressed the three dimensions of SDL (self-management, self-monitoring, and motivation) in the course design. The 18-week PEHA course consisted of four sections: health history taking, physical examination skills in various systems, case exercise and discussion, and final objective structured clinical examination (OSCE). The course content and intervention are described in detail in a previous study (Chen & Liu, 2021). Before the intervention (at the start of the semester), students were informed of the course syllabus, assignments, and the expected learning outcomes. Students were encouraged to list the tasks to be completed based on weekly learning goals. A study guide was created to facilitate tracking and recording of their learning progress based on the SDL steps. Students were asked to complete an assignment to practice their PEHA assessment and nursing diagnosis skills. The learning resources (including videos on physiology, history taking, and physical examination skills) and knowledge tests were uploaded to the Smart Master online learning platform to facilitate learning. Students practiced their skills and case exercises in the laboratory. Evaluation and feedback were provided, allowing students to revise their learning. All of the participants participated in the skills test. To evaluate their integrated communication and physical examination skills, the experimental group underwent another OSCE in the final examination.

Instruments

Five instruments were used in this study: Communication and Interpersonal Skills Rubric (IPSR), Self-Perception of PEHA Competence (SP-PEHAC) Scale, Course Satisfaction Evaluation Scale, Scales of Motivation and Learning Strategies, and the traditional Chinese version of the SDL Readiness Scale (TC-SDLRS). The IPSR (van Zanten et al., 2007), widely used to measure the doctor–patient relationship, was used in this study to evaluate communication skills. This instrument comprises 17 items in four components: skills in interviewing and collecting information, counseling and delivering information, rapport, and personal manner. Each item is rated on a 4-point Likert scale, with higher scores representing better performance. The Cronbach's alpha was .89 in this study. Two OSCE examiners rated the IPSR performance of the participants, and in case of interrater inconsistency, the interview videos were reviewed to ensure score objectivity. The interrater reliability was greater than .90.

The SP-PEHAC Scale was developed and used during the first year of this study (Chen & Liu, 2021). However, the tool was revised based on the course learning objectives and to allow the evaluation of student self-perceptions of their competencies in health history taking (seven items), physical examination skills (six items), nursing diagnosis (four items), and recording results (three items). The participants were asked to rate their perceived competency from 1 (no competence at all) to 10 (completely competent). The Cronbach's alphas for the subscale items were .81–.93 in this study. The Course Satisfaction Evaluation Scale is a self-developed 9-item tool, with each item scored from 1 to 10 to indicate the respondent's satisfaction with the course design or teaching. The Cronbach's alpha for the scale items was .93 in this study.

The Scales of Motivation and Learning Strategies developed by Cherng and Lin (2001) were used in the study. The motivation scale consists of 42 items in seven dimensions: goal orientation, job value, self-efficacy, expected success, positive emotions, negative emotions, and test anxiety. The learning strategies scale also consists of 42 items in seven sections: rehearsal, elaboration, organization, planning, monitoring, self-regulation, and evaluation strategies. Each item is rated on a 6-point Likert scale, ranging from “extremely unlikely” to “extremely likely,” with higher scores associated with better use of the strategy. In this study, the Cronbach's alpha was > .80 for all of the subscales and .91 for the overall motivation scale and was .73–.81 for the learning subscales. The test–retest reliability over a 5-week interval was .61–.70 for both inventories.

The TC-SDLRS was developed by Deng (1995), and the items were subsequently revised and reduced to improve its psychometric properties (Chen & Fan, 2023). The TC-SDLRS contains 38 items in six components: effective learning, love of learning, independent learning, learning motivation, active learning, and creative learning. Respondents score items on a 5-point Likert scale from 1 (never feel that way) to 5 (always feel that way), with a total possible instrument score of 55–275. The six-factor structure solution explains 53.3% of the total variance. The Cronbach's alpha for this instrument ranges from .89 to .72 (Chen & Fan, 2023).

Data Collection and Ethical Considerations

Ethical approval was obtained for this study from the Research Ethics Committee of China Medical University and Hospital, Taichung, Taiwan (No. CRREC-104-103[CR-1]). The purposes of this study, right to withdraw, and confidentiality policy were explained to all of the participants. Explanations and research procedures were all in compliance with institutional review board guidelines. Data collection was initiated after obtaining written informed consent from all of the participants, and data were collected between March and July 2017. Three waves of data were collected during (a) the first week of the course, (b) midterm, and (c) the 17th–18th week of the course. A research assistant arranged the schedule and collected the data by class. The participants completed the questionnaires in class and returned them to the research assistant.

Data Analysis

The data were analyzed using IBM Statistics SPSS 20.0 (IBM Inc., Armonk, NY, USA). Data were presented in terms of mean, standard deviation, and frequencies. Independent t tests were employed to examine group differences in dependent variables, and a paired t test was used to detect within-group differences. Analysis of covariance was used to control for the confounding effect of pretest scores on outcome variables. The generalized estimating equation, developed by Liang and Zeger (1986), was employed using Stata SE 9.0 version to compare between-group differences in each of the three data waves. Stata is a powerful statistical software package that allows the specification of a correlation working structure for different responses within groups and produces the average population response of regression estimates with a more accurate standard error.

Results

The mean age of the participants was 20.35 years (SD = 1.99). Most (89.4%) were women. No between-group differences were found in terms of age, gender, or work experience. However, the control group had significantly higher baseline (pretest) scores than the experimental group for the following: active learning, creative learning, independent learning, job value, self-efficacy, monitoring, organization, planning, elaboration, and physical examination (Table 1).

Table 1. - Comparison of Demographic and Baseline Data Between Groups Variable Experimental
(n = 45) Control
(n = 40) χ2 n % n % Gender 0.29  Male 4 8.9 5 12.5  Female 41 91.1 35 87.5 Work experience 0.37  Yes 12 26.7 13 32.5  No 33 73.3 27 67.5 M SD M SD χ2/t Age (years) 20.14 0.54 20.97 3.91 −0.78 Self-directed learning  Active 31.21 5.12 34.66 3.86 −3.19**  Creative 16.43 2.99 18.34 3.69 −2.49**  Independent 14.68 1.76 15.77 1.45 −2.84** Motivation  Job value 25.77 4.14 27.56 2.97 −2.09*  Self-efficacy 21.82 3.16 23.63 4.26 −2.12* Learning strategy 170.78 21.90 193.52 25.14 −4.19***  Monitoring 25.33 3.44 27.65 3.82 −2.75**  Organization 25.82 3.71 29.65 3.83 −4.35***  Planning 22.84 3.42 26.55 4.19 −4.23***  Elaboration 24.31 3.86 28.52 4.46 −4.38*** SP-PEHAC  Physical examination 26.29 10.08 30.63 6.12 −2.11**

Note. SP-PEHAC = Self-Perception of Physical Examination and Health Assessment Competence Scale.

*p < .05. **p < .01. ***p < .001.

After the intervention, the experimental group reported significantly higher scores for course satisfaction (63.86 ± 14.85) than the control group (58.24 ± 10.91; t = 2.01, p = .048). The paired t-test results revealed that history-taking skills in the experimental group had significantly improved after the intervention. As shown in Table 2, the IPSR scores for the experimental group had significantly improved between pretest and posttest, including the scores for interviewing and collecting information, counseling and delivering information, rapport, and personal manner as well as the total score. In terms of self-perceived PEHA competencies, although all scale scores for the experimental group improved after the intervention, no significant differences were found in the between-group comparisons. The findings of within-group comparisons showed the experimental group scored significantly higher on physical examination, recording results, and total score. For the control group, a difference between pretest and posttest results was found only in physical examination (Table 3).

Table 2. - Pretest–Posttest Comparisons of IPSR Scores Variable Experimental Group t Pretest Posttest M SD M SD Skills 11.13 2.42 13.52 1.78 4.35*** Counseling 6.02 2.13 12.58 2.27 11.77*** Rapport 8.61 2.09 12.72 1.92 9.20*** Personal manner 3.52 0.84 6.13 1.57 8.05*** Total 29.27 5.90 44.42 6.43 9.72***

Note. IPSR = Communication and Interpersonal Skills Rubric; Skills = skills in interviewing and collecting information; Counseling = counseling and delivering information.

***p < .001.

Table 3. - Pretest–Posttest Comparison of Self-Perceived Physical Examination and Health Assessment Competence Variable Experimental Group t Control Group t Pretest Posttest Pretest Posttest M SD M SD M SD M SD History 40.38 8.11 40.82 7.81 0.31 42.08 6.45 39.58 5.79 −1.26 PE 26.29 10.08 34.76 7.92 4.87*** 31.13 5.78 34.83 5.94 2.12* Nursing Dx 21.11 5.66 24.07 4.79 2.67 23.31 4.84 23.65 4.25 0.30 Recording 15.28 4.10 18.15 3.48 3.76*** 16.68 3.34 17.36 2.87 0.89 Total 122.72 26.83 137.48 24.37 3.04* 133.40 18.07 136.36 21.68 0.49

Note. History = health history taking; PE = physical examination skills; Dx = diagnosis.

*p < .05. ***p < .001.

In terms of motivation, learning strategies, and TC-SDLRS scores, no group differences were found in the posttest comparisons with the exception of monitoring strategy (27.86 ± 4.14 vs. 25.80 ± 3.59; t = 2.34, p = .022). After adjusting for the confounding effect of the pretest, the experimental group showed significantly more improvement in the total scores for learning strategy (F = 6.03, p = .017), elaboration (F = 4.82, p = .031), and rehearsal strategies (F = 14.03, p < .001) than the control group. The adjusted mean scores for the experimental and control groups were 184.05 versus 169.82 for the total score, 26.86 versus 24.68 for the elaboration strategy, and 27.41 versus 24.05 for the rehearsal strategy. Generalized estimating equation analysis was employed to examine differences in the scores among the three time-point measurements in the SDL and learning motivation between the two groups. After adjusting for the influence of the pretest, the SDL subscale scores for creative learning and love of learning were found to be significantly higher in the experimental group than the control group. Furthermore, in terms of learning motivation scores, the experimental group earned significantly higher scores for job value, goal orientation, and expected success and a significantly lower score for test anxiety than the control group (Table 4).

Table 4. - Differences in Learning Outcomes Between Groups Item β Coefficient Creative Learning Love of Learning Test Anxiety Job Value Goal Orientation Expected Success Time −7.11*** −0.05 −1.18*** −0.58* −0.36 −0.25 Pretest 0.02*** 0.70*** 0.73*** 0.73*** 0.73** 0.68*** Group −2.84*** −0.75* 2.08*** −0.86* −0.83* −1.20* Constant 1.14*** 6.70*** 2.43 9.64*** 9.17*** 11.67***

*p < .05. **p < .01. ***p < .001.

Discussion

The study findings reveal that integrating the SDL concept into the PEHA course has the potential to promote favorable learning outcomes. The design of this course aligned with the recommendations in the literature (ANA, 2021; Chen et al., 2013; Maniago et al., 2021). Within the nursing process construct for problem solving, the goal of the course was to cultivate assessment and physical examination skills (Maniago et al., 2021; Weber & Kelley, 2021). Student proficiency and the application of basic PEHA are essential to effectively identifying patient problems based on their acquired knowledge (Byermoen et al., 2021). In this study, students were expected to apply PEHA skills to collect the necessary subjective and objective data and then to interpret and analyze these data based on the resulting evidence or knowledge. The possible problems of patients and influential factors were identified and documented using the subjective, objective, assessment, and plan format. However, students need prior knowledge to think critically about the collected data and formulate nursing diagnoses. The case exercises in the SDL were designed based on the fundamentals of nursing and the experience of medical and surgical nursing units. Study guides designed to facilitate student learning have been found to be helpful (Chen & Liu, 2021).

No significant between-group differences were found in terms of target achievement measured using the SP-PEHAC Scale. A pretest-and-posttest difference in the control group was found only for physical examination, which is in accordance with the objective of PEHA teaching. The experimental group benefited from the case exercises and discussions and significantly improved their total score, physical examination skills, and recording results in the posttest. However, the experimental group did not earn significantly higher posttest scores for history taking or nursing diagnosis. A possible reason for this is that physical examination skills and recording results are more specific operations that are easier to learn by following operating procedures.

The study guide created to allow students to practice skills at their own pace may also have contributed to the results of this study. By contrast, health history taking and problem determination require the integration of thinking and judgment, which is more challenging to accomplish. Students need to discern the main health problems of patients during assessments and put the key clues together to identify and judge these problems based on data interpretation and analyses. Although case scenarios were provided for students to practice, they may need more time and help to discuss and practice critical thinking skills. To promote this, the faculty should balance the number of course hours dedicated to physical examination and case exercises. In addition, integrated exercises incorporating the skills of communication, history taking, physical examination, analysis, and health problem diagnosis should be created for nursing students during their clinical clerkship to strengthen their application skills on actual patient cases (Byermoen et al., 2021; M. W. Tan et al., 2021). This may also reduce the difficulty of applying PEHA skills in future clinical practice.

The results revealed that the SDL program improved the IPSR total score and its subscale scores in the experimental group. In health history taking, the four techniques that the IPSR measures are the basis of conversing with patients. Interviewing and information collection skills are essential in patient-centered interviews, and students with such skills can understand patients' problems by asking open- and closed-ended questions (Weber & Kelley, 2021). Counseling and delivering information are related to a student's ability to interact with patients using appropriate approaches, provide necessary information/suggestions, and encourage patients to engage in decision making.

Rapport relates to the student's ability to establish a caring relationship with patients, including the application of nonverbal communication skills, whereas personal manner refers to the attitudes and behaviors students show during interviews (van Zanten et al., 2007). These interpersonal skills not only are factors affecting students' physical assessment performance (Byermoen et al., 2021) but also significantly correlate with clinical ability by mediating the relationship between communication and clinical ability (Kang et al., 2021).

Nursing students tend to encounter obstacles when interacting with patients because of inadequate interpersonal communication skills, making them likely to feel uneasy, anxious, and uncertain when applying their physical assessment skills and creating confusion about their role (Maniago et al., 2021). Thus, improving interpersonal skills is the first step toward providing effective patient-centered care (ANA, 2021; Chen et al., 2013). In this study, IPSR scores in the experimental group were significantly improved after intervention, yet their perception of health history-taking competence was not significant, indicating that students were not confident in their ability to practice independently. Additional guided practice and feedback may improve student abilities related to IPSR.

The results confirmed the validity of the SDL model proposed by Garrison (1997). After implementing the SDL course, the experimental group scored significantly higher on SDL (i.e., creative learning and love of learning) than the control group. According to Garrison, the learning process involves both motivational and cognitive levels. Motivation is essential for initial learning, maintaining learning action, and achieving learning goals. Motivation is also a significant mediator between self-management and self-monitoring (Abd-El-Fattah, 2010). Students achieve more satisfactory learning outcomes when they commit to achieving a learning goal and are willing to practice it by completing their learning activities (Garrison, 1997).

Compared with the control group, the experimental group scored significantly higher in learning motivation (i.e., job value, goal orientation, and expected success) and experienced significantly lower test anxiety. These findings are consistent with previous studies, which reported SDL stimulates learning motivation (Chukwuedo et al., 2021), goal orientation ability (Li et al., 2021), and the ability to apply effective learning strategies to complete learning tasks (Y. Wang et al., 2021) in students. With clear expectations and appropriate guidelines, students may structure their SDL activities to achieve learning goals with lower test anxiety. Although the experimental group was required to complete more assignments and online learning activities, they reported a higher satisfaction with course teaching than the control group. In other words, students were more satisfied with the SDL model than the conventional lecture-based approach.

The intervention applied in this study increased job value, goal orientation, and expected success, which are all intrinsic motivations. The reason may be that the experimental group was aware of the learning objectives and focus, recognized the benefits of the course to their future jobs, and perceived the difficulty of the expected objectives to be moderate. Thus, they may have had higher expectations of success. Intrinsic motivation is also significantly associated with deep approaches, understanding the learning content, and performing meaningful learning (J.-S. Wang et al., 2015). Individuals with higher intrinsic motivation tend to exert more effort, apply more diverse learning strategies, and have more satisfactory learning outcomes (Garrison, 1997). Students with higher study engagement, learning motivation, and curiosity may also be more prone to adopt lifelong learning practices (Chukwuedo et al., 2021).

After controlling for the pretest effect, the experimental group was found to differ significantly from the control group in the use of the monitoring strategy and elaboration/rehearsal strategies. The monitoring strategy is a metacognitive strategy that refers to the degree to which individuals are able to verify their understanding of learning content through self-questioning methods (Cherng & Lin, 2001). The elaboration strategy involves thinking and behaviors related to compiling learning content and connecting new knowledge with learned knowledge for understanding. A deep approach is required to link learned content with information in long-term memory and then store it into memory. Using the monitoring strategy, students can inspect and modify the learning process to achieve the most satisfactory learning outcomes. However, the rehearsal strategy is a type of surface learning, that is, a learning strategy centered on memorizing and extracting. Because PEHA courses are offered to second-year nursing students, they must first use the rehearsal strategy to learn factual knowledge in the learning units for further application.

Limitations

This study was affected by several limitation