The Impact of AI-Powered Training on Gynecological Examination Anxiety and Satisfaction
2026年5月14日 更新者:Ayşe Gül Bursa、Fenerbahce University
Digital Transformation in Women's Health: The Impact of AI-Powered Training on Gynecological Examination Anxiety and Satisfaction
Gynecological cancers, particularly cervical, ovarian, and endometrial cancers, pose a global problem.
Cervical cancers are quite common worldwide, and this rate is even higher in developing countries.
Cervical cancers are easily treatable when detected early, and screening is quite easy.
Diagnosis is routinely made through human papillomavirus (HPV) testing and cytological screening.
Eliminating anxiety, fear, and uncertainty about gynecological examinations makes the examination process easier, thus enabling early diagnosis and treatment of diseases.
Keeping up with developing and changing technology and using it to improve women's health is an undeniable change in recent times.
This study aims to determine the effect of an AI-assisted informational training program on women's anxiety and satisfaction levels regarding gynecological examinations.
研究概览
研究类型
介入性
注册 (估计的)
114
阶段
- 不适用
参与标准
研究人员寻找符合特定描述的人,称为资格标准。这些标准的一些例子是一个人的一般健康状况或先前的治疗。
资格标准
适合学习的年龄
- 成人
- 年长者
接受健康志愿者
是的
描述
Inclusion Criteria:
- Applying to the outpatient clinic for a gynecological examination
- Being between 18-65 years of age
- Agreeing to participate in the study
Exclusion Criteria:
- Communication barrier
- Having a psychological diagnosis,
- Being pregnant
学习计划
本节提供研究计划的详细信息,包括研究的设计方式和研究的衡量标准。
研究是如何设计的?
设计细节
- 主要用途:支持治疗
- 分配:随机化
- 介入模型:并行分配
- 屏蔽:单身的
武器和干预
参与者组/臂 |
干预/治疗 |
|---|---|
|
有源比较器:intervention group
Groups that will receive AI-assisted training before gynecological examinations.
|
As an initiative, ChatGPT, one of the most commonly used artificial intelligence tools, was asked to prepare a text to provide women with detailed information before gynecological examinations.
This text was evaluated by three gynecologists specializing in the field, and necessary adjustments were made.
Based on this text, ChatGPT was asked to generate visuals for the relevant text.
Using these visuals, a 4.13-minute video was created via Canva to inform patients before their gynecological examinations.
Subtitles were added to the video, considering the potential noise level.
Women randomly assigned to the intervention group will be shown the video before their examinations.
|
|
无干预:control group
group that will not be intervened with
|
研究衡量的是什么?
主要结果指标
结果测量 |
措施说明 |
大体时间 |
|---|---|---|
|
Women's anxiety levels
大体时间:through study completion, an average of 1 hour.
|
This study aims to investigate the effect of an AI-assisted training intervention on women's anxiety levels during gynecological examinations.
The Gynecological Examination Anxiety Scale will be administered before and after the gynecological examination.
The scale consists of 20 items and is divided into 5 sub-dimensions: healthcare personnel approach, healthcare personnel experience, negative experiences, hygienic reasons, and individual attitudes.
The Cronbach's Alpha coefficient of the scale was determined to be 0.867 (Demirtop, 2014).While there are no items that are reverse-scored on the scale, a high score indicates high anxiety.
|
through study completion, an average of 1 hour.
|
|
Women's satisfaction level
大体时间:through study completion, an average of 1 hour.
|
The Outpatient Patient Satisfaction Scale, developed by Kevenk, Kantas-Yilmaz, and Ozturk, consists of 26 items and 4 dimensions: examination, diagnosis and treatment process, physical environment, appointment process, and communication.
The Cronbach's Alpha coefficient of the scale was determined to be 0.947 (Kevenk, Kantas-Yilmaz, and Ozturk, 2021).
An increase in the score obtained from the scale indicates increased satisfaction.
|
through study completion, an average of 1 hour.
|
合作者和调查者
在这里您可以找到参与这项研究的人员和组织。
研究记录日期
这些日期跟踪向 ClinicalTrials.gov 提交研究记录和摘要结果的进度。研究记录和报告的结果由国家医学图书馆 (NLM) 审查,以确保它们在发布到公共网站之前符合特定的质量控制标准。
研究主要日期
学习开始 (估计的)
2026年5月2日
初级完成 (估计的)
2026年8月30日
研究完成 (估计的)
2026年9月29日
研究注册日期
首次提交
2026年4月29日
首先提交符合 QC 标准的
2026年5月14日
首次发布 (实际的)
2026年5月20日
研究记录更新
最后更新发布 (实际的)
2026年5月20日
上次提交的符合 QC 标准的更新
2026年5月14日
最后验证
2026年4月1日
更多信息
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