Prof. Dr. Fatma A. Hegazy1*

1*Faculty of Physical Therapy, Department Of Physiotherapy, College of Health Sciences, Cairo University, Giza, Egypt


Background and Aims: This research aims to investigate the relationship between knowledge (K) and difficulty of clinical practices (DCP) while exploring the potential mediating factors of academic skills (AS), confidence (C), students’ lack of interest (SLI), and teaching skills (TS). Clinical practices are vital for educating and training healthcare professionals, and understanding the factors contributing to difficulty during these practices is crucial for improving educational outcomes.

Methodology: The study adopted a quantitative technique, collecting data from a sample of healthcare students through surveys and assessments. Statistical analyses, including mediation analysis, were conducted to examine the relationships between the variables of interest.

Results: All four mediators significantly impacted the relationship between Knowledge and Difficulty in Clinical Practices.

Limitations: The study is limited to healthcare students. The sample size of the article is limited due to time constraints, and it is not necessarily important that only mentioned mediators cofound between AS and K.

Originality: The mediators significantly impact the originality of the article, which will generate a scholarly contribution to the community.

Conclusion: The implications of these findings suggest the need for educators and policymakers to focus on enhancing academic skills, fostering confidence, addressing students’ lack of interest, and improving teaching practices.

Keywords: Knowledge, academic skills, students’ lack of interest, confidence, teaching skills, difficulty in clinical practices, experiential learning theory.


Understanding the implications of recent advancements and research on the knowledge-clinical practice link is essential to a deeper understanding of how knowledge (theoretical) and clinical practices (practical) are interrelated. The role of educators and their importance in bridging the practice-theory gap was the seclusion of Toufic’s study19. He pointed out that educators are essential for helping students use their theoretical knowledge in practice and how it can help reduce the theory-practice gap 1. Lack of time, Knowledge, confidence, skills and little formal training were the main obstacles which reduced patient outcomes in clinical practice.

This research assessed challenges in academic learning and clinical course between students. The space in the middle of theory and training was a significant detection. Practical orientation and conventional perspective of educators, students and therapists with regards to clinical capability based on practical skills that lower the demand for practice based on knowledge and research. Attempts to reduce the troubles between theory and practice in academic and clinical environments are essential to upgrade the training of student therapists. Also, education on professionalism and action based on the work environment may be helpful2. Physiotherapy Students’ academic knowledge may increase due to the strategic plans of the unique characteristics of teaching styles. The learning styles can help students, and this improvising should be understood by the educators about their ways of teaching and learning simultaneously3. This study identified 107 competencies as a minimal requirement for clinical practice for physiotherapists working in UK critical care units. The results of this study need to be disseminated to assist training initiatives in higher education and the healthcare industry that will hopefully lessen variation in clinical practice4.

Physiotherapy is concerned with the theoretical and clinical educational processes. Clinical practice is associated with the academic knowledge and clinical skills received in the learning environment. Moreover, according to Günay & Kılınç5, clinical skills play a significant role in implementing theoretical knowledge into clinical practices.

This study shows that the quality of health and wellness programs largely depends upon a person’s knowledge, skills, and attitude, and to enhance the knowledge and skills of therapists must continue with great vigor and regularly evaluated in clinical practise. A therapist should have good knowledge to understand the actual condition so that he can manage perfectly6.

The need to comprehend the relationship between physical therapists’ and occupational therapists’ knowledge and their clinical practices in clinics, as well as the potential effects of mediators on this relationship, is the topic this study attempts to solve. Despite the significance of clinical practice and knowledge in physical therapy and occupational therapy, an in-depth study into the precise variables that influence the connection between knowledge and clinical practices is lacking. This study seeks to contribute to developing evidence-based methods to improve the standard of clinical practices26 and optimize patient outcomes in physical therapy and occupational therapy settings by investigating these correlations and locating potential mediators. This study aims to assess the association of clinical practices of physical / occupational therapists in clinics in association with their knowledge. Another objective of this study is to find the effects of mediators between knowledge and clinical practices.


Clinical practice experiences have a broad role for the students of rehabilitation. Their knowledge directly relates to what they will practice at clinics throughout their studies. However, rehab students need to know deep insight into clinical practices through their understanding. If it still needs to be done, it is essential to assess the mediators reflecting the knowledge and its impacts on clinical practices.

Conceptual Framework

H0: There is no association between Knowledge and Difficulty in Clinical Practices.

H1: There is an association between Knowledge and Difficulty in Clinical Practices.

H2: There is an association between Knowledge and Academic Skills.

H3: There is an association between Knowledge and Confidence.

H4: There is an association between Knowledge and Students’ Lack of Interest.

H5: There is an association between Knowledge and Teaching Skills.

H6: There is an association between Academic Skills and Difficulty in Clinical Practices.

H7: There is an association between Confidence and Difficulty in Clinical Practices.

H8: There is an association between Students’ Lack of Interest and Difficulty in Clinical Practices.

H9: There is an association between Teaching Skills and Difficulty in Clinical Practices.


Study Setting 

The study is conducted at Rehabilitation Colleges in all locations respectively.

Study Design 

Cross-Sectional Study

Target Population 

Students in 3rd year and onwards who have already done their clinical rotations.

Duration of Study 

The duration of the study is 6 to 8 months after the approval of the synopsis.

Sampling Technique 

A simple Random Sampling technique will be used in this study.

Inclusion Criteria 

Students of 3rd, 4th and 5th year who have experienced clinical rotations.

Exclusion Criteria 

1st and 2nd-year students who still need to do clinical rotations.

Data Collection Tool 

The data of the study was collected through a self-administrated questionnaire. Total of 4 questions for each variable have been constructed and extracted from previous multiple studies. A total of 28 questions were included in the questionnaire based on two parts, i.e., demographics and variables.

Data Collection Procedure 

Data was collected through online Google forms from the students of Rehabilitation College.

Data Analysis

Data was analyzed through Smart PLS. To test the demographics of the data, descriptive statistics will be used. Moreover, the validity and reliability of the data will be checked through discriminant validity and convergent validity. To check the association between the variables, path coefficient test will be used.

Public Health Significance

The study will help the society to get awareness regarding the difficulties faced by students during their clinical practices. The study will explore the mediating components of knowledge and clinical practices.


The table shows the descriptive statistics of a sample population based on gender, age, and occupation. The sample size is 260 individuals, with 30 (11.5%) male and 230 (88.5%) female. In terms of age, 12 (4.6%) individuals are between the ages of 18 and 20, 212 (81.5%) are between the ages of 21 and 23, 34 (13.07%) are between the ages of 24 and 26, and only 2 (0.76%) are 27 and above. Regarding occupation, 235 (90.3%) individuals are in physical therapy, while 25 (9.61%) are in occupational therapy. These descriptive statistics provide an overview of the sample population and can be used to gain insights into the characteristics of the population under study.

Measurement and Model Assessment

The data taken from the respondents of Rehabilitation Students have been tested through multiple tests by the PLS model. Smart PLS have been used to assess the data and test the model’s adequacy.

Discriminate and Convergent Validity

The reliability of outer items has been tested through external loadings. According to Henseler, J., Ringle, C. M., & Sinkovics R. R7, the value of outer loadings should be greater than 0.70 to consider them consistent. In this model, 27 items were concluded to have 0.70 or greater loading. This measurement tests the degree between two measures or constructs which should be theoretically related. The AVE values should be greater than 0.50 to achieve convergent validity. The value of Cronbach Alpha and Composite Reliability should be greater than 0.708, but not greater than 0.90 because it would be measured as the same construct.

Internal Consistency

Internal consistency evaluates the reliability of constructs. According to Hasan et al.9, composite reliability and Cronbach alpha should be greater than 0.70 and less than 0.90. The above table shows that composite reliability and Cronbach alpha values lie between 0.70 and 0.90, indicating that the measure of the construct is valid. Convergent validity: The convergent validity model is used to observe the degree of inter-relationship between the underlying constructs. The average variance extracted (AVE) was proposed by Fornell and Larcker10, which states that AVE should be greater than 0.50. Table 2 shows AVE of each construct has achieved the suggested criteria. Discriminant validity: Discriminant validity test analyses relationships between latent variables. According to Moor et al.11, results less than 0.85 indicate that discriminant validity exists, and greater than 0.85 suggests that the two constructs greatly overlap. The Fornell and Larcker criterion of AVE value should be greater than 0.50. Although, composite reliability and Cronbach alpha should exceed 0.70. Also, each loading should be higher than 0.70 so that this measurement model analyzes the relationship between latent variables and their measures.

Fornell and Larcker Criterion

Moreover, according to Fornell et al.10, the square root of AVE should be more than the collinearity of other constructs to achieve discriminant validity. The above shows the variance among all the latent variables in this study.

Cross Loadings and Outer VIF Measurement Model

To judge the discriminant validity, cross loadings are used to interpret the data. It interprets the associations of factor loading of one construct to another.

The evaluation of cross-loadings is a common technique for establishing discriminant validity. Researchers constantly analyze indicator loading patterns to recognize evidence that they possess high loadings on identical factors and those that load highly on other factors12. The result in Table 4 indicates apparent that researchers accomplished discriminant validity as it means that the factor loading of an associated contrast is greater than the factor loading on another difference.

Moreover, VIF is used to check the collinearity. The values of VIF should be less than 5 to prove the collinearity between the indicators24.

Variance inflation factor (VIF) determines that the estimated regression coefficient is present if the variance is inflated in the correlation of the independent variables13. Furthermore, IF is used to check collinearity. The VIF values should be lesser than 5 to prove the collinearity among indicators. Mention table shows that values are less than 5, proving that the collinearity is absent among indicators24.

The above study distinctly shows that the Coefficient of the interval has brought off the desired results following our hypothesis testing; knowledge has a significant negative effect on clinical practice (Co-eff: 0.72, P value: 0.023, T value: 2.578), while knowledge also has a significant positive impact on academic skills (Co-eff: 0.67, P value: 0.0002, T value: 5.67). Knowledge has very strong positive relationship with confidence (Co-eff: 0.81, P value: 0.01, T value: 5.43). Furthermore, knowledge significantly negatively affects skills (Co-eff: 0.75, P value: 0.0001, T value: 10.41). The hypothesis of the relation between knowledge and teaching skills has been rejected because the value of P is more than 0.1 (P value: 0.453), and T value is less than 2.5 (T value: 0.453), and the value of the Coefficient is 0.66, which is positive, whereas academic skills have a significant negative effect on clinical practice (Co-eff: 0.53, P value: 0.0001, T value: 6.75). Confidence has a significant relationship with Clinical Practice, which is negative (Co-eff: 0.75, P value: 0.0011, T value: 3.54), and clinical practice has significantly affected the skills of a clinician, and the relationship is positive (Co-eff: 0.66, P value: 0.0001, T value: 4.66). Teaching skills significantly negatively impact clinical practice (C0-eff: 0.54, P value: 0.0001, T value: 6.78).

Model Quality Assessments

Standardized Root Mean Square Residual (SRMR)

According to Shiu et al., 14, the SRMR is used to check the quality of the model in which the value should be less than 0.10. In this study, the value of SRMR was found to be 0.093, which is estimated to be a good fit.


The results shed important light on the nuanced connection between knowledge and the complexity of clinical practises. First, it’s crucial to understand that pupils’ difficulty level is not just determined by their expertise15. Our research uncovered some mediators that significantly impacted how knowledge and difficulty related to one another. The capacity of students to apply their theoretical knowledge in real-world settings emerged as a critical mediator, indicating that this ability had a considerable impact on the degree of difficulty encountered during clinical practises. Strong academic skills enabled students to transmit their information more successfully, allowing them to overcome the challenges presented in real-world healthcare circumstances. A study conducted by Alsaban concluded that sometimes students’ lack of interest might lead to poor knowledge16.

A key mediator in the link between knowledge and difficulty, confidence also played an important part. Students who felt confident in their skills generally went into clinical settings with a more upbeat attitude, which enhanced motivation and resilience in the face of challenges17. However, even if they have the necessary knowledge, students who need more confidence may find the clinical practice more complex, which could negatively affect their performance as a whole 18.

The lack of interest among students was another essential factor in our study. Lack of interest can result from many factors, such as a mismatch between individual career goals and the healthcare profession of choice or disconnect between academic knowledge and its practical application in clinical settings. Lack of interest on the part of students may make it harder for them to participate entirely in clinical practices, making their whole experience more challenging.

Additionally, it became clear that teaching abilities were a crucial mediating factor in the association between clinical practice difficulty and knowledge. Effective teaching methods can improve students’ information retention and application, leading to better performance in clinical settings. On the other hand, effective teaching strategies may make it easier for students to connect theory and practice, hence raising the perceived complexity of clinical experiences.


This study emphasizes the significance of considering mediators when analyzing the association between clinical practice difficulties and knowledge. Academic skills, confidence, lack of interest, and teaching skills significantly impacted students’ struggles during clinical practice. By addressing these mediators, educators and policymakers can lessen students’ difficulties, enhancing their educational opportunities and general clinical performance.

Limitations and Future Implications

The study is only focused on the students related to allied healthcare workers, and it could also be extended to other healthcare workers21. The sample size of the study is limited due to time constraints. The study can be assessed in a longitudinal manner22. Moreover, the mentioned mediators are limited to the developed model and adapting only these variables does not accurately define the model’s causality and directionality. More mediators can be tested in Study23.


Conception or Design: Prof. Dr. Fatma A. Hegazy

Acquisition, Analysis or Interpretation of Data: Prof. Dr. Fatma A. Hegazy

Manuscript Writing & Approval: Prof. Dr. Fatma A. Hegazy

The author acknowledge his/her accountability for all facets of the research, ensuring that any concerns regarding the accuracy or integrity of the work are duly investigated and resolved.

ACKNOWLEDGEMENTS: I would like to thanks all the colleagues and participants who supported me in the production of this manuscript.

INFORMED CONSENT: The informed consent was not applied

CONFLICT OF INTEREST: The authors have no conflict of interest.

FUNDING STATEMENTS: No funding has been received during this research.

ETHICS STATEMENTS: Each respondent of the study is informed through a consent form added in the starting of the questionnaire. It was ensured that all the collected information will keep enclosed and it will be only used for this study purpose.


  1. Hussein MH, Osuji J. Bridging the theory-practice dichotomy in nursing: The role of nurse educators. J. Nurs. Educ. Pract. 2017;7(3):20-5.
  2. Rubertone PP, Nixon-Cave K, Wellmon R. Influence of Clinical Instructor Experience on Assessing Doctor of Physical Therapist Student Clinical Performance: A Mixed-Methods Study. Journal of Physical Therapy Education. 2022 Mar 1;36(1):25-33.
  3. İlçin N, Tomruk M, Yeşilyaprak SS, Karadibak D, Savcı S. The relationship between learning styles and academic performance in TURKISH physiotherapy students. BMC medical education. 2018 Dec;18(1):1-8.
  4. Twose P, Jones U, Cornell G. Minimum standards of clinical practice for physiotherapists working in critical care settings in the United Kingdom: a modified Delphi technique. Journal of the Intensive Care Society. 2019 May;20(2):118-31.
  5. Günay U, Kılınç G. The transfer of theoretical knowledge to clinical practice by nursing students and the difficulties they experience: A qualitative study. Nurse education today. 2018 Jun 1;65:81-6.
  6. Shakeri K, Fallahi-Khoshknab M, Khankeh H, Hosseini M, Heidari M. Knowledge, attitude, and clinical skill of emergency medical technicians from Tehran emergency center in trauma exposure. International journal of critical illness and injury science. 2018 Oct;8(4):188.
  7. Henseler J. Ringle, CM & Sinkovics, RR (2009). The use of partial least squares path modeling in international marketing. Advances in International Marketing.;20:277-319.
  8. Hair Jr J, Sarstedt M, Hopkins L, G. Kuppelwieser V. Partial least squares structural equation modeling (PLS-SEM) An emerging tool in business research. European business review. 2014 Mar 4;26(2):106-21.
  9. Hasan Z, Naeem M, Ahmed S, Zeerak S. Impact of Strategic Ambiguity Tagline on Billboard Advertising on Consumers Attention. Market Forces. 2022 Jun 26;17(1):163-84.
  10. Fornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research. 1981 Feb;18(1):39-50.
  11. Moor CC, Oppenheimer JC, Nakshbandi G, Aerts JG, Brinkman P, Maitland-van der Zee AH, Wijsenbeek MS. Exhaled breath analysis by use of eNose technology: a novel diagnostic tool for interstitial lung disease. European Respiratory Journal. 2021 Jan 1;57(1).
  12. Marsh HW, Guo J, Dicke T, Parker PD, Craven RG. Confirmatory factor analysis (CFA), exploratory structural equation modeling (ESEM), and set-ESEM: Optimal balance between goodness of fit and parsimony. Multivariate behavioral research. 2020 Jan 2;55(1):102-19.
  13. O’brien RM. A caution regarding rules of thumb for variance inflation factors. Quality & quantity. 2007 Oct;41:673-90.
  14. Shi D, Maydeu-Olivares A, Rosseel Y. Assessing fit in ordinal factor analysis models: SRMR vs. RMSEA. Structural Equation Modeling: A Multidisciplinary Journal. 2020 Jan 2;27(1):1-5.
  15. Zhao W, He L, Deng W, Zhu J, Su A, Zhang Y. The effectiveness of the combined problem-based learning (PBL) and case-based learning (CBL) teaching method in the clinical practical teaching of thyroid disease. BMC medical education. 2020 Dec;20:1-0.
  16. Palopo, i., 2022. Factor analysis of students’ lack of interest in online English learning of SMKN 1 luwu timur during the covid-19 pandemic. ‌
  17. Fischer H, Amelung D, Said N. The accuracy of German citizens’ confidence in their climate change knowledge. Nature Climate Change. 2019 Oct;9(10):776-80.
  18. Ulenaers D, Grosemans J, Schrooten W, Bergs J. Clinical placement experience of nursing students during the COVID-19 pandemic: A cross-sectional study. Nurse education today. 2021 Apr 1;99:104746.
  19. Toufic J. What Was I Thinking?. Berlin: Sternberg Press; 2017 Sep 8.
  20. Maydeu-Olivares A, Shi D, Rosseel Y. Assessing fit in structural equation models: A Monte-Carlo evaluation of RMSEA versus SRMR confidence intervals and tests of close fit. Structural Equation Modeling: A Multidisciplinary Journal. 2018 May 4;25(3):389-402.
  21. Aloisio, L.D., Gifford, W.A., McGilton, K.S., Lalonde, M., Estabrooks, C.A. and Squires, J.E., 2018. Individual and organizational predictors of allied healthcare providers’ job satisfaction in residential long-term care. BMC health services research, 18, pp.1-18.
  22. Polanin JR, Espelage DL, Grotpeter JK, Spinney E, Ingram KM, Valido A, El Sheikh A, Torgal C, Robinson L. A meta-analysis of longitudinal partial correlations between school violence and mental health, school performance, and criminal or delinquent acts. Psychological bulletin. 2021 Feb;147(2):115.
  23. Wang Y, Duan Z, Ma Z, Mao Y, Li X, Wilson A, Qin H, Ou J, Peng K, Zhou F, Li C. Epidemiology of mental health problems among patients with cancer during COVID-19 pandemic. Translational psychiatry. 2020 Jul 31;10(1):263.
  24. Senaviratna NA, A Cooray TM. Diagnosing multicollinearity of logistic regression model. Asian Journal of Probability and Statistics. 2019 Oct 1;5(2):1-9.
  25. Cheung GW, Wang C. Current approaches for assessing convergent and discriminant validity with SEM: Issues and solutions. InAcademy of management proceedings 2017 (Vol. 2017, No. 1, p. 12706). Briarcliff Manor, NY 10510: Academy of Management.
  26. Fusar-Poli P, Sullivan SA, Shah JL, Uhlhaas PJ. Improving the detection of individuals at clinical risk for psychosis in the community, primary and secondary care: an integrated evidence-based approach. Frontiers in psychiatry. 2019 Oct 24;10:774.

The Ziauddin University is on the list of I4OA, I4OC, and JISC.

This is an open- access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0).