2025
Clinical Reasoning in Nursing
Name: Clinical Reasoning in Nursing
Code: ENF14449L
6 ECTS
Duration: 15 weeks/156 hours
Scientific Area:
Nursing
Teaching languages: Portuguese
Languages of tutoring support: Portuguese, English
Regime de Frequência: Presencial
Presentation
This UC contributes, through its programmatic contents, to the understanding of what clinical reasoning is and what it represents in the process of care.
Sustainable Development Goals
Learning Goals
Objectives
1. Understand what clinical reasoning is and what it represents in the caring process
2. Understand the procedural dimension of clinical reasoning in decision-making
3. Understand different models of decision-making
Skills
• Demonstrates understanding of the need for clinical reasoning
• Know the concepts that structure the clinical reasoning
• Knows the decision models
• Can recognize specific clinical data according to a theoretical framework
• Recognizes different sources of data and information
• Can think critically about clinical data
• Can analyze data in the light of theoretical knowledge or scientific evidence
• Can associate analytical reasoning with intuitive reasoning
• Can perform diagnostic judgments
• Can reflect on the decisions you have made
1. Understand what clinical reasoning is and what it represents in the caring process
2. Understand the procedural dimension of clinical reasoning in decision-making
3. Understand different models of decision-making
Skills
• Demonstrates understanding of the need for clinical reasoning
• Know the concepts that structure the clinical reasoning
• Knows the decision models
• Can recognize specific clinical data according to a theoretical framework
• Recognizes different sources of data and information
• Can think critically about clinical data
• Can analyze data in the light of theoretical knowledge or scientific evidence
• Can associate analytical reasoning with intuitive reasoning
• Can perform diagnostic judgments
• Can reflect on the decisions you have made
Contents
The clinical nursing environments
• The nature of the clinical problems
• The concepts of clinical data, clinical reasoning, diagnostic reasoning, clinical judgments, diagnostic evaluation, decisíon making, critical thinking, the nursing process and clinical supervision
• The collection of clinical data (sources and strategies)
• Strategies for analyzing clinical data (modalities of critical thinking) Analytical thinking, Inductive and Deductive and, Intuitive thinking
• The factual, evaluative and interpretive analysis of clinical data
Exercises The collection and analyzing clinical data performed by students under the supervision of teachers
• the clinical reasoning in the diagnostic evaluation and the clinical reasoning in the decisíon making
• Strategies for analyzing clinical data
• Decisions in simulated context
• Decisions in the action
• The relevance of a standardized language
• Reflection in action and on action
• The nature of the clinical problems
• The concepts of clinical data, clinical reasoning, diagnostic reasoning, clinical judgments, diagnostic evaluation, decisíon making, critical thinking, the nursing process and clinical supervision
• The collection of clinical data (sources and strategies)
• Strategies for analyzing clinical data (modalities of critical thinking) Analytical thinking, Inductive and Deductive and, Intuitive thinking
• The factual, evaluative and interpretive analysis of clinical data
Exercises The collection and analyzing clinical data performed by students under the supervision of teachers
• the clinical reasoning in the diagnostic evaluation and the clinical reasoning in the decisíon making
• Strategies for analyzing clinical data
• Decisions in simulated context
• Decisions in the action
• The relevance of a standardized language
• Reflection in action and on action
Teaching Methods
The lectures are expository and present the available knowledge about the clinical reasoning process and its relevance to decision making. The practical classes are used for discussion and analysis of everyday situations and that mirror human health experiences. Each situation is analyzed and interpreted using the knowledge of other courses and / or scientific evidence. The use of databases is done from the judgment prepared based on simulated data. Laboratory practices training instrumental skills from simulated situations.
In this course unit, which focuses on the development of clinical reasoning, critical thinking, and decision-making in nursing, the use of Artificial Intelligence (AI) tools is permitted to support study, systematisation of ideas, synthesis and organisation of literature, and support for scientific language/linguistic correction. However, these tools may not at any time replace the student's cognitive process. The use of AI must respect the principles of academic integrity, professional responsibility and clinical safety, in accordance with the institutional guidelines for the use of AI in Higher Education (ORDER No. 34/2026 - Guidelines for the use of Artificial Intelligence (AI) in the teaching, assessment and learning process at UÉ).
Therefore, the following uses are considered unacceptable:
- Generating complex clinical responses and/or nursing diagnoses;
- Preparing comprehensive clinical case analyses without using the cognitive process;
- Inclusion of clinical/personal or confidential data in AI tools;
- Submission of texts/papers generated entirely by AI;
- Use of AI in formal assessments (e.g. tests/exams);
- Construction of references or citations without verification.
Its misuse will be classified as academic fraud under Article 119 of the Academic Regulations (Code of Conduct, Fraud and Plagiarism).
Whenever AI is used in assessed work, a statement must be included: ?In preparing this work, the tool (name) was used for (purpose). All content has been critically reviewed by the author, who assumes final responsibility.?
In this course unit, which focuses on the development of clinical reasoning, critical thinking, and decision-making in nursing, the use of Artificial Intelligence (AI) tools is permitted to support study, systematisation of ideas, synthesis and organisation of literature, and support for scientific language/linguistic correction. However, these tools may not at any time replace the student's cognitive process. The use of AI must respect the principles of academic integrity, professional responsibility and clinical safety, in accordance with the institutional guidelines for the use of AI in Higher Education (ORDER No. 34/2026 - Guidelines for the use of Artificial Intelligence (AI) in the teaching, assessment and learning process at UÉ).
Therefore, the following uses are considered unacceptable:
- Generating complex clinical responses and/or nursing diagnoses;
- Preparing comprehensive clinical case analyses without using the cognitive process;
- Inclusion of clinical/personal or confidential data in AI tools;
- Submission of texts/papers generated entirely by AI;
- Use of AI in formal assessments (e.g. tests/exams);
- Construction of references or citations without verification.
Its misuse will be classified as academic fraud under Article 119 of the Academic Regulations (Code of Conduct, Fraud and Plagiarism).
Whenever AI is used in assessed work, a statement must be included: ?In preparing this work, the tool (name) was used for (purpose). All content has been critically reviewed by the author, who assumes final responsibility.?
Assessment
A. CONTINUOUS ASSESSMENT
The assessment of the Course Unit will include:
i. Individual Written Exam: Weighting of 40%;
ii. Written Group Work with in-class presentation: Weighting of 40% (has its own guidelines for its preparation ? Annex I), with the Final Grade for the Work = Written Work 20% + Oral Presentation 20%, with mandatory attendance of all group members;
iii. Clinical Cases/Exercises carried out during the theoretical classes: 20%.
The Final Grade (CF) of the Course Unit results from the application of the following formula: CF = (Attendance x 0.4) + (Written Group Work x 0.20 + Oral Presentation x 0.20) + (Clinical Cases/Exercises x 0.20).
To pass the course, the student must have a minimum of 9.5 in each of the following assessment components: Written Exam and Group Work. All assessment moments comply with the academic regulations of the University of Évora.
Failure to complete the Written Exam and/or Group Work will result in failing the course.
B. ASSESSMENT BY EXAMINATION
The assessment by examination, in any of the scheduled periods, will be carried out through an individual written exam, which includes the analysis of a clinical case. The final grade will be quantitative (0 to 20 points), and the student will pass if they obtain a grade equal to or greater than 9.5 points (Final Grade of the Course = Grade obtained in the individual written exam).
The assessment of the Course Unit will include:
i. Individual Written Exam: Weighting of 40%;
ii. Written Group Work with in-class presentation: Weighting of 40% (has its own guidelines for its preparation ? Annex I), with the Final Grade for the Work = Written Work 20% + Oral Presentation 20%, with mandatory attendance of all group members;
iii. Clinical Cases/Exercises carried out during the theoretical classes: 20%.
The Final Grade (CF) of the Course Unit results from the application of the following formula: CF = (Attendance x 0.4) + (Written Group Work x 0.20 + Oral Presentation x 0.20) + (Clinical Cases/Exercises x 0.20).
To pass the course, the student must have a minimum of 9.5 in each of the following assessment components: Written Exam and Group Work. All assessment moments comply with the academic regulations of the University of Évora.
Failure to complete the Written Exam and/or Group Work will result in failing the course.
B. ASSESSMENT BY EXAMINATION
The assessment by examination, in any of the scheduled periods, will be carried out through an individual written exam, which includes the analysis of a clinical case. The final grade will be quantitative (0 to 20 points), and the student will pass if they obtain a grade equal to or greater than 9.5 points (Final Grade of the Course = Grade obtained in the individual written exam).
Teaching Staff
- Marta Sofia Inácio Catarino
- Rute Isabel Quadrado Pires [responsible]
Accreditations and Certifications
