Nursing StudentsAI-VRSNCT07518199
The Effect of an Artificial Intelligence-Supported Virtual Reality Simulation on Nursing Students' Holistic Care Skills
A study testing AI-VRS in people with Nursing Students and Decision-Making, run by Gazi University at 1 site in Turkey (Türkiye).
Trial summary
This study aims to evaluate the effect of artificial intelligence (AI)-supported virtual reality (VR) simulation on nursing students' holistic care skills. The study is a randomised controlled trial involving fourth-year nursing students, divided into an experimental and a control group. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Nursing Students, Decision-Making, Diagnostic Reasoning
- What is being tested?
- AI-VRS
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 80 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- Apr 2026to May 2026
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Who may be able to join
- Voluntary participation in the study,
- Having enrolled for the first time in the courses HEM 402 Professional Practice I and HEM 404 Professional Practice II in the Department of Nursing, Faculty of Health Sciences,
- Absence of eye conditions affecting depth perception, such as amblyopia (lazy eye), anisometropia(different refractive errors in each eye), and strabismus (squint). (Self- report is accepted.),
- Academic performance score between 2.00 and 4.00.,
Who cannot join
- Having received training in holistic care skills in addition to their undergraduate nursing degree,
- Having experience with virtual simulation exercises focused on holistic care skills,
- Holding a high school, foundation year or undergraduate degree in a health-related field,
- Having difficulty understanding and speaking Turkish,
- Criteria for Exclusion from the Study:
- The participant has not completed or has incompletely completed the required forms and scales,
Show all 10Show fewer
- The participant in the experimental group had not taken part in or completed the AI-supported virtual reality simulation,
- Students in the control group did not take part in the educational case study,
- Students in the experimental and control groups did not take part in the assessment case study,
- The participant wishes to withdraw from the study,
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Turkey (Türkiye)1 site
Gazi University Nursing Faculty
Ankara, 06500
Nurcan Çalışkan, Prof., PhD, +0312 216 26 53, nurcany@gazi.edu.tr
Özlem Tikit, Research Assistant, PhD student, +90 0554 756 58 04, ozlemtikit@gazi.edu.tr
About this study
This study aims to evaluate the effect of artificial intelligence (AI)-supported virtual reality (VR) simulation on nursing students' holistic care skills. The study is a randomised controlled trial involving fourth-year nursing students, divided into an experimental and a control group. Whilst the experimental group will receive AI-supported VR simulation training, the control group will receive traditional case-based training. Outcomes to be assessed include decision-making, symptom identification, nursing diagnosis, simulation design and satisfaction with the training methods.
Read the detailed description
This study will evaluate the effect of an artificial intelligence-supported virtual reality (VR) simulation on nursing students' holistic care skills. The study is designed as a pre-post, parallel-group, randomised controlled trial involving 80 fourth-year nursing students (40 in the experimental group and 40 in the control group). Eligible participants will complete a demographic form and the Melbourne Decision-Making Scale at the outset. Participants will be stratified by overall academic grade point average and prior VR experience, and randomly assigned to groups by an independent statistician.
Whilst the intervention group receives AI-supported VR simulation training, the control group will receive traditional case-based training using the same case scenario to ensure comparability. Both the training case and the assessment case, along with the assessment criteria, will be developed based on expert consensus.
Two weeks after the intervention, both groups will complete a case study assessment. Data collection will include the Melbourne Decision-Making Scale, nursing diagnosis and symptom identification results, and satisfaction measures. The statistician will be blinded, and appropriate statistical tests will be applied based on the data distribution.
- Conditions
- Nursing Students, Decision-Making, Diagnostic Reasoning
- Being tested
- AI-VRS, Control
- Keywords
- Artificial Intelligence, Simulation Training, Immersive Virtual Reality, Nursing Education, Holistic Care, Nursing Diagnoses, Educational Technology
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-VRSOther
This intervention consists of an AI-supported virtual reality (VR) simulation designed to improve nursing students' holistic care skills. Participants interact with a virtual patient to perform patient history-taking, identify symptoms, and formulate nursing diagnoses across the dimensions of holistic care (physical, psychological, social, and spiritual). The simulation is delivered using Meta Quest 3 VR headsets and incorporates artificial intelligence to provide dynamic, responsive patient interactions. The intervention includes structured simulation scenarios with high-fidelity graphics and interactive decision-making processes to support skill acquisition.
ControlOther
This intervention consists of traditional case-based training delivered through presentations and question-and-answer discussions. Participants will analyse case scenarios and receive feedback from instructors. This approach provides a practical learning experience without using VR technology.
| Study group | Role | What they receive |
|---|---|---|
| AI-VRS | Experimental | At the start of the study, participants will be asked to complete a demographic questionnaire and the Melbourne Decision-Making Scale. They will then be assigned to research groups based on their overall academic grade point average and their experience with virtual reality (VR) headsets. Before the intervention, participants will be provided with a pre-intervention information guide. The experimental group will undergo training using an AI-supported VR simulation designed to facilitate taking a patient history, identifying symptoms, and determining a nursing diagnosis. Two weeks after the training, concurrently with the control group, they will undertake an assessment case study in which they must analyse the case individually, without instructor support, to identify nursing diagnoses and symptoms. They will then complete the Melbourne Decision-Making Scale, the Simulation Design Scale, and a satisfaction questionnaire regarding the training methods. Receives: Other: AI-VRS |
| Control | Other | At baseline, participants will complete a demographic questionnaire and the Melbourne Decision-Making Scale. Participants will then be allocated to study groups using a stratified randomisation approach based on academic grade point average and prior experience with VR headsets.
The control group will receive traditional case-based training using written clinical scenarios. Following the training, a two-week interval will be observed. After this period, both the control and experimental groups will complete an assessment case simultaneously. During this assessment, participants will be required to independently analyse the case, identify the patient's symptoms, and formulate appropriate nursing diagnoses without instructor support.
After completing the assessment, participants will again complete the Melbourne Decision-Making Scale as a post-test measure. Receives: Other: Control |
What is being measured
The primary outcome is the main question the study is designed to answer.
Nursing Diagnosis and Symptom Identification within a Holistic Care Framework
Participants' ability to correctly identify patient symptoms and formulate appropriate nursing diagnoses will be evaluated using a structured assessment form. In addition to overall accuracy, performance will be assessed based on the inclusion of multiple dimensions of holistic care (physical, psychological, social, and spiritual). Higher scores will indicate greater diagnostic accuracy in nursing and more comprehensive holistic care assessment.
Measured: 2 weeks post-intervention (assessment case study)
Secondary outcomes (3)
Melbourne Decision-Making Scale
Used to assess changes in decision-making skills. Higher scores indicate better decision-making performance.The Melbourne Decision-Making Scale consists of two parts. MKVÖ I: This scale is designed to assess self-esteem (self-confidence) in decision-making. The maximum score on the scale is 12. High scores indicate high self-esteem in decision-making. MKVÖ II: The scale measures decision-making styles. In the scoring of MKVÖ II, the following score ranges are used: cautious (0-12), avoidant (0-12), procrastinating (0-10), and panicked (0-10).
Measured: Baseline (pre-intervention) and 2 weeks post-intervention (assessment case study)
Simulation Design Scale
Assessed using the Simulation Design Scale to evaluate participants' perceptions of the simulation design. Higher scores indicate more positive evaluations. The scale consists of five subscales-"Goals and Knowledge," "Support," "Problem Solving," "Feedback/Guided Reflection," and "Authenticity"-and twenty items. The number of items in each subscale is as follows: the "Goals and Knowledge" subscale has 5 items, the "Support" subscale has 4 items, the "Problem Solving" subscale has 5 items, the "Feedback/Guided Reflection" subscale has 4 items, and the "Degree of Authenticity" subscale has 2 items. The scale, administered in two sections, measures students' opinions on whether the best simulation design elements were implemented in the simulation application in the first section and on the extent to which the simulation application is important to students in the second section.
Measured: 2 weeks post-intervention
Satisfaction Survey Regarding Teaching Methods
Student satisfaction will be assessed immediately after completion of the VR training to capture participants' perceptions of the training experience. Higher scores indicate greater satisfaction.The survey is scored on a scale of 16 to 80 points.
Measured: 2 weeks post-intervention
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Apr 2026
- Study startApr 2026 (estimated)
- Primary completionApr 2026 (estimated)
- Study completionMay 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (8)Papers the sponsor has linked to this study.
- Other trials for Nursing StudentsStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- Parallel-group, pre-test/post-test randomized controlled trial.
- Masking
- None (open label)
- Enrolment
- 80 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Gazi University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Nurcan Çalışkan, Gazi University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07518199
- Sponsor's protocol ID
- 2025-1069
- Other IDs
- 2025-10282, 2025
- Acronym
- AI-VRS
- Status last verified
- Mar 2026
- First submitted
- 30 Mar 2026
- Last updated
- 8 Apr 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Hopewell S, Chan AW, Collins GS, Hrobjartsson A, Moher D, Schulz KF, Tunn R, Aggarwal R, Berkwits M, Berlin JA, Bhandari N, Butcher NJ, Campbell MK, Chidebe RCW, Elbourne D, Farmer A, Fergusson DA, Golub RM, Goodman SN, Hoffmann TC, Ioannidis JPA, Kahan BC, Knowles RL, Lamb SE, Lewis S, Loder E, Offringa M, Ravaud P, Richards DP, Rockhold FW, Schriger DL, Siegfried NL, Staniszewska S, Taylor RS, Thabane L, Torgerson D, Vohra S, White IR, Boutron I. CONSORT 2025 statement: updated guideline for reporting randomised trials. BMJ. 2025 Apr 14;389:e081123. doi: 10.1136/bmj-2024-081123.
PubMed 40228833INACSL Standards Committee, Decker, S., Sapp, A., Bibin, L., Chidume, T., Crawford, S. B., Fayyaz, J., Johnson, B. K., & Szydlowski, J. (2025d). Healthcare Simulation Standards of Best Practice®: The Debriefing Process. Clinical Simulation in Nursing, 105, 101775-101775. https://doi.org/10.1016/j.ecns.2025.101775
INACSL Standards Committee, DiGregorio, H., Todd, A., Blackwell, B., Brennan, B. A., Repsha, C., Shelton, C. M., Vaughn, J., Wands, L., Wruble, E., & Yeager, C. (2025c). Healthcare Simulation Standards of Best PracticeⓇ Facilitation. Clinical Simulation in Nursing, https://doi.org/10.1016/j.ecns.2025.101776
INACSL Standards Committee, Watts, P.I, McDermott, D.S., Alinier, G., Charnetski, M., Ludlow, J., Horsley, E., Meakim, C., & Nawathe, P. (2021b). Healthcare Simulation Standards of Best Practice® Simulation Design. Clinical Simulation in Nursing, https://doi.org/10.1016/j.ecns.2021.08.009.
INACSL Standards Committee. (2021a). Healthcare Simulation Standard of Best Practice® Prebriefing: Preparation and briefing Persico, Lori et al. Clinical Simulation in Nursing, Volume 105, 101777. https://doi.org/10.1016/j.ecns.2025.101777 1876-1399
INACSL Standards Committee, Persico, L., Wilson-Keates, B., DiGregorio, H., Decker, S., & Xavier, N. (2025a). Preamble: Grounded in Excellence: The Cornerstone Healthcare Simulation Standards of Best Practice®. Clinical Simulation in Nursing, https://doi.org/10.1016/j.ecns.2025.101774
INACSL Standards Committee, Persico, L., Ramakrishnan, S., Wilson-Keates, B., Catena, R., Charnetski, M., Fogg, N., Jones, M. C., Ludlow, J., MacLean, H., Simmons, V. C., Smeltzer, S., & Wilk, A. (2025b). Healthcare Simulation Standard of Best Practice® Prebriefing: Preparation and briefing. Clinical Simulation in Nursing https://doi.org/10.1016/j.ecns.2025.101777
Ackley, B. J., & Ladwig, G. B. (2024). Hemşirelik tanıları el kitabı: Bakım planlamasında kanıta dayalı rehber (Z. Göçmen Baykara, N. Çalışkan, E. Gülnar, E. Sarıtaş, & G. Eyüboğlu, Ed. ve çev., 13. baskı). Ankara: Nobel Tıp Kitabevleri. ISBN:978-625-6448-92-6.
Study at a glance
From the registry record, last updated by the sponsor on 8 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The Effect of an Artificial Intelligence-Supported Virtual Reality Simulation on Nursing Students' Holistic Care Skills
- ClinicalTrials.gov ID
- NCT07518199
- Acronym / sponsor ID
- AI-VRS / 2025-1069
- Conditions
- Nursing Students, Decision-Making, Diagnostic Reasoning
- Keywords
- Artificial Intelligence, Simulation Training, Immersive Virtual Reality, Nursing Education, Holistic Care, Nursing Diagnoses, Educational Technology
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 80 participants (estimated)
- Lead sponsor
- Gazi UniversityUniversity, hospital or other
- Study start
- Apr 2026 (estimated)
- Primary completion
- Apr 2026 (estimated)
- Study completion
- May 2026 (estimated)
- First posted
- 8 Apr 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
This study aims to evaluate the effect of artificial intelligence (AI)-supported virtual reality (VR) simulation on nursing students' holistic care skills. The study is a randomised controlled trial involving fourth-year nursing students, divided into an experimental and a control group. Whilst the experimental group will receive AI-supported VR simulation training, the control group will receive traditional case-based training. Outcomes to be assessed include decision-making, symptom identification, nursing diagnosis, simulation design and satisfaction with the training methods.
Detailed description
This study will evaluate the effect of an artificial intelligence-supported virtual reality (VR) simulation on nursing students' holistic care skills. The study is designed as a pre-post, parallel-group, randomised controlled trial involving 80 fourth-year nursing students (40 in the experimental group and 40 in the control group). Eligible participants will complete a demographic form and the Melbourne Decision-Making Scale at the outset. Participants will be stratified by overall academic grade point average and prior VR experience, and randomly assigned to groups by an independent statistician.
Whilst the intervention group receives AI-supported VR simulation training, the control group will receive traditional case-based training using the same case scenario to ensure comparability. Both the training case and the assessment case, along with the assessment criteria, will be developed based on expert consensus.
Two weeks after the intervention, both groups will complete a case study assessment. Data collection will include the Melbourne Decision-Making Scale, nursing diagnosis and symptom identification results, and satisfaction measures. The statistician will be blinded, and appropriate statistical tests will be applied based on the data distribution.
Study design
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
- Model description
- Parallel-group, pre-test/post-test randomized controlled trial.
| Arm | Type | Receives |
|---|---|---|
| AI-VRS | Experimental | Other: AI-VRSAt the start of the study, participants will be asked to complete a demographic questionnaire and the Melbourne Decision-Making Scale. They will then be assigned to research groups based on their overall academic grade point average and their experience with virtual reality (VR) headsets. Before the intervention, participants will be provided with a pre-intervention information guide. The experimental group will undergo training using an AI-supported VR simulation designed to facilitate taking a patient history, identifying symptoms, and determining a nursing diagnosis. Two weeks after the training, concurrently with the control group, they will undertake an assessment case study in which they must analyse the case individually, without instructor support, to identify nursing diagnoses and symptoms. They will then complete the Melbourne Decision-Making Scale, the Simulation Design Scale, and a satisfaction questionnaire regarding the training methods. |
| Control | Other | Other: ControlAt baseline, participants will complete a demographic questionnaire and the Melbourne Decision-Making Scale. Participants will then be allocated to study groups using a stratified randomisation approach based on academic grade point average and prior experience with VR headsets. The control group will receive traditional case-based training using written clinical scenarios. Following the training, a two-week interval will be observed. After this period, both the control and experimental groups will complete an assessment case simultaneously. During this assessment, participants will be required to independently analyse the case, identify the patient's symptoms, and formulate appropriate nursing diagnoses without instructor support. After completing the assessment, participants will again complete the Melbourne Decision-Making Scale as a post-test measure. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (4 listed)
- Voluntary participation in the study,
- Having enrolled for the first time in the courses HEM 402 Professional Practice I and HEM 404 Professional Practice II in the Department of Nursing, Faculty of Health Sciences,
- Absence of eye conditions affecting depth perception, such as amblyopia (lazy eye), anisometropia(different refractive errors in each eye), and strabismus (squint). (Self- report is accepted.),
- Academic performance score between 2.00 and 4.00.,
Key exclusion criteria (10 listed)
- Having received training in holistic care skills in addition to their undergraduate nursing degree,
- Having experience with virtual simulation exercises focused on holistic care skills,
- Holding a high school, foundation year or undergraduate degree in a health-related field,
- Having difficulty understanding and speaking Turkish,
- The participant has not completed or has incompletely completed the required forms and scales,
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-VRSThis intervention consists of an AI-supported virtual reality (VR) simulation designed to improve nursing students' holistic care skills. Participants interact with a virtual patient to perform patient history-taking, identify symptoms, and formulate nursing diagnoses across the dimensions of holistic care (physical, psychological, social, and spiritual). The simulation is delivered using Meta Quest 3 VR headsets and incorporates artificial intelligence to provide dynamic, responsive patient interactions. The intervention includes structured simulation scenarios with high-fidelity graphics and interactive decision-making processes to support skill acquisition. | Other | AI-VRS |
| ControlThis intervention consists of traditional case-based training delivered through presentations and question-and-answer discussions. Participants will analyse case scenarios and receive feedback from instructors. This approach provides a practical learning experience without using VR technology. | Other | Control |
Primary outcome
Nursing Diagnosis and Symptom Identification within a Holistic Care Framework
Time frame: 2 weeks post-intervention (assessment case study)
Participants' ability to correctly identify patient symptoms and formulate appropriate nursing diagnoses will be evaluated using a structured assessment form. In addition to overall accuracy, performance will be assessed based on the inclusion of multiple dimensions of holistic care (physical, psychological, social, and spiritual). Higher scores will indicate greater diagnostic accuracy in nursing and more comprehensive holistic care assessment.
3 secondary outcomes
Melbourne Decision-Making Scale
Time frame: Baseline (pre-intervention) and 2 weeks post-intervention (assessment case study)
Simulation Design Scale
Time frame: 2 weeks post-intervention
Satisfaction Survey Regarding Teaching Methods
Time frame: 2 weeks post-intervention
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 Apr 2026.
- Background: PubMed 40228833Hopewell S, Chan AW, Collins GS, Hrobjartsson A, Moher D, Schulz KF, Tunn R, Aggarwal R, Berkwits M, Berlin JA, Bhandari N, Butcher NJ, Campbell MK, Chidebe RCW, Elbourne D, Farmer A, Fergusson DA, Golub RM, Goodman SN, Hoffmann TC, Ioannidis JPA, Kahan BC, Knowles RL, Lamb SE, Lewis S, Loder E, Offringa M, Ravaud P, Richards DP, Rockhold FW, Schriger DL, Siegfried NL, Staniszewska S, Taylor RS, Thabane L, Torgerson D, Vohra S, White IR, Boutron I. CONSORT 2025 statement: updated guideline for reporting randomised trials. BMJ. 2025 Apr 14;389:e081123. doi: 10.1136/bmj-2024-081123.
- BackgroundINACSL Standards Committee, Decker, S., Sapp, A., Bibin, L., Chidume, T., Crawford, S. B., Fayyaz, J., Johnson, B. K., & Szydlowski, J. (2025d). Healthcare Simulation Standards of Best Practice®: The Debriefing Process. Clinical Simulation in Nursing, 105, 101775-101775. https://doi.org/10.1016/j.ecns.2025.101775
- BackgroundINACSL Standards Committee, DiGregorio, H., Todd, A., Blackwell, B., Brennan, B. A., Repsha, C., Shelton, C. M., Vaughn, J., Wands, L., Wruble, E., & Yeager, C. (2025c). Healthcare Simulation Standards of Best PracticeⓇ Facilitation. Clinical Simulation in Nursing, https://doi.org/10.1016/j.ecns.2025.101776
- BackgroundINACSL Standards Committee, Watts, P.I, McDermott, D.S., Alinier, G., Charnetski, M., Ludlow, J., Horsley, E., Meakim, C., & Nawathe, P. (2021b). Healthcare Simulation Standards of Best Practice® Simulation Design. Clinical Simulation in Nursing, https://doi.org/10.1016/j.ecns.2021.08.009.
- BackgroundINACSL Standards Committee. (2021a). Healthcare Simulation Standard of Best Practice® Prebriefing: Preparation and briefing Persico, Lori et al. Clinical Simulation in Nursing, Volume 105, 101777. https://doi.org/10.1016/j.ecns.2025.101777 1876-1399
- BackgroundINACSL Standards Committee, Persico, L., Wilson-Keates, B., DiGregorio, H., Decker, S., & Xavier, N. (2025a). Preamble: Grounded in Excellence: The Cornerstone Healthcare Simulation Standards of Best Practice®. Clinical Simulation in Nursing, https://doi.org/10.1016/j.ecns.2025.101774
- BackgroundINACSL Standards Committee, Persico, L., Ramakrishnan, S., Wilson-Keates, B., Catena, R., Charnetski, M., Fogg, N., Jones, M. C., Ludlow, J., MacLean, H., Simmons, V. C., Smeltzer, S., & Wilk, A. (2025b). Healthcare Simulation Standard of Best Practice® Prebriefing: Preparation and briefing. Clinical Simulation in Nursing https://doi.org/10.1016/j.ecns.2025.101777
- BackgroundAckley, B. J., & Ladwig, G. B. (2024). Hemşirelik tanıları el kitabı: Bakım planlamasında kanıta dayalı rehber (Z. Göçmen Baykara, N. Çalışkan, E. Gülnar, E. Sarıtaş, & G. Eyüboğlu, Ed. ve çev., 13. baskı). Ankara: Nobel Tıp Kitabevleri. ISBN:978-625-6448-92-6.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing AI-VRS in people with Nursing Students and Decision-Making, run by Gazi University at 1 site in Turkey (Türkiye).
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 10 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 8 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Turkey (Türkiye). Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Nurcan Çalışkan, Prof., PhD (+90 312 216 2653, nurcany@gazi.edu.tr), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Nursing Diagnosis and Symptom Identification within a Holistic Care Framework (time frame: 2 weeks post-intervention (assessment case study)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07518199, which also lists 8 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 8 Apr 2026.