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Research article
Huan-Fang Lee
National Cheng Kung University
Corresponding Author
ORCiD: https://orcid.org/0000-0002-1276-4409
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Studies in the past have identified factors related to the nursing staff’s intention to leave the unit, institution, and profession. However, none has successfully predicted the nurse's intention to quit the job (NIQJ). Whether NIQJ can be predicted be predicted is an interesting topic in healthcare management. A model to predict the NIQJ for novice nurses in hospitals should be investigated and developed in this mobile computer age.
Objective: The aim of this study is to build a model to develop an app for automatic prediction and classification of NIQJ using a smaller number of items to help assess NIQJ and take necessary actions before nurses quit the job.
Methods: We recruited 1104 novice nurses working in six medical centers in Taiwan to complete 100-item questionnaires related to NIQJ in October 2018. The k-mean was used to divide nurses into two classes (i.e., NIQJ and Non- NIQJ) based on five- NIQJ items regarding leave intention. Feature variables were chosen from 100 relevant items. Two models, including artificial neural network (ANN) and convolutional neural network (CNN), were compared across four scenarios made up by two training sets (n=1104 and n=804B) and their corresponding testing (n=300a) sets to verify the model accuracy (e.g., sensitivity, specificity, area under the receiver operating characteristic curve, AUC) and stability and generalization (e.g., using the training set to predict the testing set). An app predicting NIQJ was then developed involving the model's estimated parameters as a website assessment.
Results: We observed that (1) 24 feature variables extracted from this study in ANN model yielded a higher AUC of 0.82 (95% CI 0.80-0.84) based on the total 1104 cases, (2) the ANN performed better than CNN on both accuracy, stability and generalization, and (3) an ready and available app for predicting NIQJ was successfully developed in this study.
Conclusions: The 24-item ANN model with the 53 parameters estimated by the ANN for improving the accuracy of NIQJ has been developed with the use of Excel (Microsoft Corp). The app would help team leader and HR department to pick up nurse’s NIQJ before actions are taken, allowing them to make plans accordingly.
Keywords
nurse intention to quit the job, artificial neural network, convolutional neural network, nurse, Microsoft Excel, receiver operating characteristic curve
Figure 1
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Figure 3
Figure 4
Figure 5
This is a list of supplementary files associated with this preprint. Click to download.
- questionnaireenglishversion.docx
Additional File 1 Study questionnaires(Word format)
- data.txt
Additional File 2 Study 1104-case dataset(text file)
- abstractMP4.txt
Additional File x 3 Abstract video (MP4) at https://youtu.be/wDeBy3f4PHU
- onlineapp.txt
Additional File 4 App Online assessing nurse intention to quit the job at http://www.healthup.org.tw/irs/irsin_e.asp?type1=95
- MPRSAscheme.png
Additional File 5 MPPSA schmem(JPG)
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CURRENT STATUS: Under Review
Version 1
Posted 26 Jan, 2021
No community comments so far
Editorial decision: Major revision
On 04 Mar, 2021
Review #2 received
Received 03 Mar, 2021
Review #3 received
Received 01 Mar, 2021
Reviewer #3 agreed
On 17 Feb, 2021
Reviewer #2 agreed
On 10 Feb, 2021
Review #1 received
Received 25 Jan, 2021
Reviewer #1 agreed
On 24 Jan, 2021
Editor assigned
On 23 Jan, 2021
Reviewers invited
Invitations sent on 23 Jan, 2021
Submission checks complete
On 21 Jan, 2021
Editor invited
On 18 Jan, 2021
First submitted
On 16 Jan, 2021
Metrics
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Subject Areas
Medical Informatics
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A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
This preprint is under consideration at BMC Medical Informatics and Decision Making. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Huan-Fang Lee
National Cheng Kung University
Corresponding Author
ORCiD: https://orcid.org/0000-0002-1276-4409
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 26 Jan, 2021
No community comments so far
Editorial decision: Major revision
On 04 Mar, 2021
Review #2 received
Received 03 Mar, 2021
Review #3 received
Received 01 Mar, 2021
Reviewer #3 agreed
On 17 Feb, 2021
Reviewer #2 agreed
On 10 Feb, 2021
Review #1 received
Received 25 Jan, 2021
Reviewer #1 agreed
On 24 Jan, 2021
Editor assigned
On 23 Jan, 2021
Reviewers invited
Invitations sent on 23 Jan, 2021
Submission checks complete
On 21 Jan, 2021
Editor invited
On 18 Jan, 2021
First submitted
On 16 Jan, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Medical Informatics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Studies in the past have identified factors related to the nursing staff’s intention to leave the unit, institution, and profession. However, none has successfully predicted the nurse's intention to quit the job (NIQJ). Whether NIQJ can be predicted be predicted is an interesting topic in healthcare management. A model to predict the NIQJ for novice nurses in hospitals should be investigated and developed in this mobile computer age.
Objective: The aim of this study is to build a model to develop an app for automatic prediction and classification of NIQJ using a smaller number of items to help assess NIQJ and take necessary actions before nurses quit the job.
Methods: We recruited 1104 novice nurses working in six medical centers in Taiwan to complete 100-item questionnaires related to NIQJ in October 2018. The k-mean was used to divide nurses into two classes (i.e., NIQJ and Non- NIQJ) based on five- NIQJ items regarding leave intention. Feature variables were chosen from 100 relevant items. Two models, including artificial neural network (ANN) and convolutional neural network (CNN), were compared across four scenarios made up by two training sets (n=1104 and n=804B) and their corresponding testing (n=300a) sets to verify the model accuracy (e.g., sensitivity, specificity, area under the receiver operating characteristic curve, AUC) and stability and generalization (e.g., using the training set to predict the testing set). An app predicting NIQJ was then developed involving the model's estimated parameters as a website assessment.
Results: We observed that (1) 24 feature variables extracted from this study in ANN model yielded a higher AUC of 0.82 (95% CI 0.80-0.84) based on the total 1104 cases, (2) the ANN performed better than CNN on both accuracy, stability and generalization, and (3) an ready and available app for predicting NIQJ was successfully developed in this study.
Conclusions: The 24-item ANN model with the 53 parameters estimated by the ANN for improving the accuracy of NIQJ has been developed with the use of Excel (Microsoft Corp). The app would help team leader and HR department to pick up nurse’s NIQJ before actions are taken, allowing them to make plans accordingly.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
This is a list of supplementary files associated with this preprint. Click to download.
- questionnaireenglishversion.docx
Additional File 1 Study questionnaires(Word format)
- data.txt
Additional File 2 Study 1104-case dataset(text file)
- abstractMP4.txt
Additional File x 3 Abstract video (MP4) at https://youtu.be/wDeBy3f4PHU
- onlineapp.txt
Additional File 4 App Online assessing nurse intention to quit the job at http://www.healthup.org.tw/irs/irsin_e.asp?type1=95
- MPRSAscheme.png
Additional File 5 MPPSA schmem(JPG)
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