{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T07:19:35Z","timestamp":1776842375197,"version":"3.51.2"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1007\/s10489-024-05305-4","type":"journal-article","created":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T03:02:12Z","timestamp":1709262132000},"page":"3606-3628","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["ResMFuse-Net: Residual-based multilevel fused network with spatial\u2013temporal features for hand hygiene monitoring"],"prefix":"10.1007","volume":"54","author":[{"given":"Sohaib","family":"Asif","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyi","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuehan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fengxiao","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yusen","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,3,1]]},"reference":[{"issue":"3","key":"5305_CR1","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1111\/j.1365-3156.2006.01568.x","volume":"11","author":"T Rabie","year":"2006","unstructured":"Rabie T, Curtis V (2006) Handwashing and risk of respiratory infections: a quantitative systematic review. Tropical Med Int Health 11(3):258\u2013267","journal-title":"Tropical Med Int Health"},{"issue":"8","key":"5305_CR2","doi-asserted-by":"publisher","first-page":"1641","DOI":"10.4315\/0362-028X-71.8.1641","volume":"71","author":"C Strohbehn","year":"2008","unstructured":"Strohbehn C, Sneed J, Paez P, Meyer J (2008) Hand washing frequencies and procedures used in retail food services. J Food Prot 71(8):1641\u20131650","journal-title":"J Food Prot"},{"issue":"1","key":"5305_CR3","doi-asserted-by":"publisher","first-page":"97","DOI":"10.3390\/ijerph8010097","volume":"8","author":"M Burton","year":"2011","unstructured":"Burton M, Cobb E, Donachie P, Judah G, Curtis V, Schmidt W-P (2011) The effect of handwashing with water or soap on bacterial contamination of hands. Int J Environ Res Public Health 8(1):97\u2013104","journal-title":"Int J Environ Res Public Health"},{"key":"5305_CR4","doi-asserted-by":"crossref","unstructured":"Stilo A, Troiano G, Melcarne L, Gioffr\u00e8 ME, Nante N, Messina G, et al (2016) Hand washing in operating room: a procedural comparison. Epidemiology. Epidemiol Biostat Public Health 13(3)","DOI":"10.2427\/11734"},{"issue":"6","key":"5305_CR5","doi-asserted-by":"publisher","first-page":"1730","DOI":"10.1016\/j.jaad.2020.07.057","volume":"83","author":"CW Rundle","year":"2020","unstructured":"Rundle CW, Presley CL, Militello M, Barber C, Powell DL, Jacob SE et al (2020) Hand hygiene during COVID-19: recommendations from the american contact dermatitis society. J Am Acad Dermatol 83(6):1730\u20131737","journal-title":"J Am Acad Dermatol"},{"issue":"6","key":"5305_CR6","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1049\/htl2.12018","volume":"8","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Xue T, Liu Z, Chen W, Vanrumste B (2021) Detecting hand washing activity among activities of daily living and classification of WHO hand washing techniques using wearable devices and machine learning algorithms. Healthcare Technol Lett 8(6):148\u2013158","journal-title":"Healthcare Technol Lett"},{"key":"5305_CR7","unstructured":"Organization WH (2009) WHO guidelines on hand hygiene in health care. WHO guidelines on hand hygiene in health care. p 270"},{"issue":"8","key":"5305_CR8","doi-asserted-by":"publisher","first-page":"6484","DOI":"10.1007\/s12144-021-01985-0","volume":"42","author":"CK Lao","year":"2023","unstructured":"Lao CK, Li X, Zhao N, Gou M, Zhou G (2023) Using the health action process approach to predict facemask use and hand washing in the early stages of the COVID-19 pandemic in China. Curr Psychol 42(8):6484\u20136493","journal-title":"Curr Psychol"},{"issue":"1","key":"5305_CR9","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1093\/intqhc\/mzy099","volume":"31","author":"S Awwad","year":"2019","unstructured":"Awwad S, Tarvade S, Piccardi M, Gattas DJ (2019) The use of privacy-protected computer vision to measure the quality of healthcare worker hand hygiene. Int J Qual Health Care 31(1):36\u201342","journal-title":"Int J Qual Health Care"},{"key":"5305_CR10","doi-asserted-by":"publisher","first-page":"13659","DOI":"10.1109\/JSEN.2023.3271297","volume":"23","author":"S Shrimali","year":"2023","unstructured":"Shrimali S, Teuscher C (2023) A novel deep learning, camera, and sensor-based system for enforcing hand hygiene compliance in healthcare facilities. IEEE Sens J. 23:13659\u201313670","journal-title":"IEEE Sens J."},{"issue":"3","key":"5305_CR11","doi-asserted-by":"publisher","first-page":"e17001","DOI":"10.2196\/17001","volume":"8","author":"C Wang","year":"2020","unstructured":"Wang C, Sarsenbayeva Z, Chen X, Dingler T, Goncalves J, Kostakos V (2020) Accurate measurement of handwash quality using sensor armbands: instrument validation study. JMIR Mhealth Uhealth 8(3):e17001","journal-title":"JMIR Mhealth Uhealth"},{"issue":"7","key":"5305_CR12","doi-asserted-by":"publisher","first-page":"7608","DOI":"10.1109\/JSEN.2023.3244582","volume":"23","author":"K Abubeker","year":"2023","unstructured":"Abubeker K, Baskar S (2023) A hand hygiene tracking system with LoRaWAN network for the abolition of hospital-acquired infections. IEEE Sens J 23(7):7608\u20137615","journal-title":"IEEE Sens J"},{"key":"5305_CR13","doi-asserted-by":"publisher","first-page":"100171","DOI":"10.1016\/j.smhl.2020.100171","volume":"19","author":"S Samyoun","year":"2021","unstructured":"Samyoun S, Shubha SS, Mondol MAS, Stankovic JA (2021) iWash: a smartwatch handwashing quality assessment and reminder system with real-time feedback in the context of infectious disease. Smart Health 19:100171","journal-title":"Smart Health"},{"key":"5305_CR14","doi-asserted-by":"publisher","first-page":"103368","DOI":"10.1016\/j.bspc.2021.103368","volume":"72","author":"MZ Amrani","year":"2022","unstructured":"Amrani MZ, Borst CW, Achour N (2022) Multi-sensory assessment for hand pattern recognition. Biomed Signal Process Control 72:103368","journal-title":"Biomed Signal Process Control"},{"issue":"9","key":"5305_CR15","doi-asserted-by":"publisher","first-page":"2024","DOI":"10.3390\/electronics12092024","volume":"12","author":"R \u00d6zakar","year":"2023","unstructured":"\u00d6zakar R, Gedikli E (2023) Evaluation of hand washing procedure using vision-based frame level and spatio-temporal level data models. Electronics 12(9):2024","journal-title":"Electronics"},{"issue":"4","key":"5305_CR16","doi-asserted-by":"publisher","first-page":"3268","DOI":"10.1109\/TITS.2020.3034239","volume":"23","author":"C Chen","year":"2020","unstructured":"Chen C, Li K, Zhongyao C, Piccialli F, Hoi SC, Zeng Z (2020) A hybrid deep learning based framework for component defect detection of moving trains. IEEE Trans Intell Transp Syst 23(4):3268\u20133280","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"5305_CR17","unstructured":"Ivanovs M, Kadikis R, Lulla M, Rutkovskis A, Elsts A (2020) Automated quality assessment of hand washing using deep learning. arXiv preprint arXiv:201111383"},{"issue":"9","key":"5305_CR18","doi-asserted-by":"publisher","first-page":"170","DOI":"10.3390\/jimaging7090170","volume":"7","author":"C Zhong","year":"2021","unstructured":"Zhong C, Reibman AR, Mina HA, Deering AJ (2021) Designing a computer-vision application: a case study for hand-hygiene assessment in an open-room environment. J Imaging 7(9):170","journal-title":"J Imaging"},{"issue":"03","key":"5305_CR19","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.imed.2022.03.005","volume":"2","author":"T Wang","year":"2022","unstructured":"Wang T, Xia J, Wu T, Ni H, Long E, Li J-PO et al (2022) Handwashing quality assessment via deep learning: a modelling study for monitoring compliance and standards in hospitals and communities. Intell Med 2(03):152\u2013160","journal-title":"Intell Med"},{"key":"5305_CR20","unstructured":"Yeung S, Alahi A, Haque A, Peng B, Luo Z, Singh A, et al (2016) Vision-based hand hygiene monitoring in hospitals. AMIA2016"},{"key":"5305_CR21","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1007\/s00138-009-0234-7","volume":"22","author":"DF Llorca","year":"2011","unstructured":"Llorca DF, Parra I, Sotelo M\u00c1, Lacey G (2011) A vision-based system for automatic hand washing quality assessment. Mach Vis Appl 22:219\u2013234","journal-title":"Mach Vis Appl"},{"key":"5305_CR22","unstructured":"Kim M, Choi J, Kim N (2020) Fully automated hand hygiene monitoring\\\\in operating room using 3d convolutional neural network. arXiv preprint arXiv:200309087"},{"key":"5305_CR23","doi-asserted-by":"crossref","unstructured":"Zhong C, Reibman AR, Cordoba HM, Deering AJ (2019) Hand-hygiene activity recognition in egocentric video. 2019 IEEE 21st International Workshop on Multimedia Signal Processing (MMSP): IEEE. p 1\u20136","DOI":"10.1109\/MMSP.2019.8901753"},{"key":"5305_CR24","doi-asserted-by":"publisher","first-page":"103651","DOI":"10.1016\/j.bspc.2022.103651","volume":"76","author":"T Xie","year":"2022","unstructured":"Xie T, Tian J, Ma L (2022) A vision-based hand hygiene monitoring approach using self-attention convolutional neural network. Biomed Signal Process Control 76:103651","journal-title":"Biomed Signal Process Control"},{"key":"5305_CR25","doi-asserted-by":"publisher","first-page":"119588","DOI":"10.1016\/j.eswa.2023.119588","volume":"218","author":"MA Haghpanah","year":"2023","unstructured":"Haghpanah MA, Vali S, Torkamani AM, Masouleh MT, Kalhor A, Sarraf EA (2023) Real-time hand rubbing quality estimation using deep learning enhanced by separation index and feature-based confidence metric. Expert Syst Appl 218:119588","journal-title":"Expert Syst Appl"},{"issue":"8","key":"5305_CR26","doi-asserted-by":"publisher","first-page":"1316","DOI":"10.1093\/jamia\/ocaa115","volume":"27","author":"A Singh","year":"2020","unstructured":"Singh A, Haque A, Alahi A, Yeung S, Guo M, Glassman JR et al (2020) Automatic detection of hand hygiene using computer vision technology. J Am Med Inform Assoc 27(8):1316\u20131320","journal-title":"J Am Med Inform Assoc"},{"issue":"11","key":"5305_CR27","doi-asserted-by":"publisher","first-page":"120","DOI":"10.3390\/jimaging6110120","volume":"6","author":"C Zhong","year":"2020","unstructured":"Zhong C, Reibman AR, Mina HA, Deering AJ (2020) Multi-view hand-hygiene recognition for food safety. J Imaging 6(11):120","journal-title":"J Imaging"},{"issue":"4","key":"5305_CR28","doi-asserted-by":"publisher","first-page":"38","DOI":"10.3390\/data6040038","volume":"6","author":"M Lulla","year":"2021","unstructured":"Lulla M, Rutkovskis A, Slavinska A, Vilde A, Gromova A, Ivanovs M et al (2021) Hand-washing video dataset annotated according to the world health organization\u2019s hand-washing guidelines. Data 6(4):38","journal-title":"Data"},{"key":"5305_CR29","unstructured":"Shi X, Chen Z, Wang H, Yeung D-Y, Wong W-K, Woo W-C (2015) Convolutional LSTM network: a machine learning approach for precipitation nowcasting. Adv Neural Inf Process Syst 28"},{"issue":"1","key":"5305_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-46850-0","volume":"9","author":"SA Rahman","year":"2019","unstructured":"Rahman SA, Adjeroh DA (2019) Deep learning using convolutional LSTM estimates biological age from physical activity. Sci Rep 9(1):1\u201315","journal-title":"Sci Rep"},{"key":"5305_CR31","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1016\/j.neucom.2018.10.095","volume":"396","author":"M Majd","year":"2020","unstructured":"Majd M, Safabakhsh R (2020) Correlational convolutional LSTM for human action recognition. Neurocomputing 396:224\u2013229","journal-title":"Neurocomputing"},{"key":"5305_CR32","doi-asserted-by":"publisher","first-page":"100412","DOI":"10.1016\/j.imu.2020.100412","volume":"20","author":"MZ Islam","year":"2020","unstructured":"Islam MZ, Islam MM, Asraf A (2020) A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images. Inf Med Unlock 20:100412","journal-title":"Inf Med Unlock"},{"key":"5305_CR33","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.neucom.2022.11.039","volume":"520","author":"S Deepak","year":"2023","unstructured":"Deepak S, Ameer P (2023) Brain tumor categorization from imbalanced MRI dataset using weighted loss and deep feature fusion. Neurocomputing 520:94\u2013102","journal-title":"Neurocomputing"},{"key":"5305_CR34","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.inffus.2020.07.006","volume":"64","author":"Y-D Zhang","year":"2020","unstructured":"Zhang Y-D, Dong Z, Wang S-H, Yu X, Yao X, Zhou Q et al (2020) Advances in multimodal data fusion in neuroimaging: overview, challenges, and novel orientation. Information Fusion 64:149\u2013187","journal-title":"Information Fusion"},{"key":"5305_CR35","doi-asserted-by":"publisher","first-page":"105866","DOI":"10.1016\/j.bspc.2023.105866","volume":"90","author":"S Asif","year":"2024","unstructured":"Asif S, Zhao M, Tang F, Zhu Y (2024) DCDS-net: deep transfer network based on depth-wise separable convolution with residual connection for diagnosing gastrointestinal diseases. Biomed Signal Process Control 90:105866","journal-title":"Biomed Signal Process Control"},{"key":"5305_CR36","doi-asserted-by":"publisher","first-page":"5633","DOI":"10.1007\/s00521-019-04311-9","volume":"32","author":"X Zou","year":"2020","unstructured":"Zou X, Zhou L, Li K, Ouyang A, Chen C (2020) Multi-task cascade deep convolutional neural networks for large-scale commodity recognition. Neural Comput Appl 32:5633\u20135647","journal-title":"Neural Comput Appl"},{"issue":"4","key":"5305_CR37","doi-asserted-by":"publisher","first-page":"2890","DOI":"10.1109\/TII.2020.3025592","volume":"17","author":"Y Li","year":"2020","unstructured":"Li Y, Chen C, Duan M, Zeng Z, Li K (2020) Attention-aware encoder\u2013decoder neural networks for heterogeneous graphs of things. IEEE Trans Industr Inf 17(4):2890\u20132898","journal-title":"IEEE Trans Industr Inf"},{"key":"5305_CR38","doi-asserted-by":"crossref","unstructured":"Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition. p 1251\u20138","DOI":"10.1109\/CVPR.2017.195"},{"key":"5305_CR39","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE conference on computer vision and pattern recognition. p 4510\u201320","DOI":"10.1109\/CVPR.2018.00474"},{"issue":"3","key":"5305_CR40","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1007\/s13246-020-00888-x","volume":"43","author":"D Das","year":"2020","unstructured":"Das D, Santosh K, Pal U (2020) Truncated inception net: COVID-19 outbreak screening using chest X-rays. Phys Eng Sci Med 43(3):915\u2013925","journal-title":"Phys Eng Sci Med"},{"issue":"4","key":"5305_CR41","doi-asserted-by":"publisher","first-page":"1323","DOI":"10.1109\/TNNLS.2019.2919764","volume":"31","author":"G Zhu","year":"2019","unstructured":"Zhu G, Zhang L, Yang L, Mei L, Shah SAA, Bennamoun M et al (2019) Redundancy and attention in convolutional LSTM for gesture recognition. IEEE Trans Neural Netw Learn Syst 31(4):1323\u20131335","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"5305_CR42","first-page":"448","volume-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"S Ioffe","year":"2015","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. PMLR, International conference on machine learning, pp 448\u201356"},{"key":"5305_CR43","unstructured":"Agarap AF (2018) Deep learning using rectified linear units (relu). arXiv preprint arXiv:180308375"},{"key":"5305_CR44","unstructured":"Boureau Y-L, Ponce J, LeCun Y (2010) A theoretical analysis of feature pooling in visual recognition. Proceedings of the 27th international conference on machine learning (ICML-10). p 111\u20138"},{"key":"5305_CR45","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition. p 770\u20138","DOI":"10.1109\/CVPR.2016.90"},{"issue":"5","key":"5305_CR46","doi-asserted-by":"publisher","first-page":"2197","DOI":"10.1007\/s00500-021-06579-3","volume":"26","author":"VSK Tangudu","year":"2022","unstructured":"Tangudu VSK, Kakarla J, Venkateswarlu IB (2022) COVID-19 detection from chest x-ray using MobileNet and residual separable convolution block. Soft Comput 26(5):2197\u20132208","journal-title":"Soft Comput"},{"key":"5305_CR47","doi-asserted-by":"publisher","first-page":"1138","DOI":"10.1109\/TIFS.2019.2936913","volume":"15","author":"R Zhang","year":"2019","unstructured":"Zhang R, Zhu F, Liu J, Liu G (2019) Depth-wise separable convolutions and multi-level pooling for an efficient spatial CNN-based steganalysis. IEEE Trans Inf Forensics Secur 15:1138\u20131150","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"5305_CR48","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the IEEE international conference on computer vision. p 1026\u201334","DOI":"10.1109\/ICCV.2015.123"},{"issue":"3","key":"5305_CR49","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1007\/s41664-018-0068-2","volume":"2","author":"Y Xu","year":"2018","unstructured":"Xu Y, Goodacre R (2018) On splitting training and validation set: a comparative study of cross-validation, bootstrap and systematic sampling for estimating the generalization performance of supervised learning. J Anal Test 2(3):249\u2013262","journal-title":"J Anal Test"},{"key":"5305_CR50","unstructured":"Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:14126980"},{"issue":"1","key":"5305_CR51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-018-0151-6","volume":"5","author":"JL Leevy","year":"2018","unstructured":"Leevy JL, Khoshgoftaar TM, Bauder RA, Seliya N (2018) A survey on addressing high-class imbalance in big data. J Big Data 5(1):1\u201330","journal-title":"J Big Data"},{"key":"5305_CR52","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. Proceedings of the IEEE conference on computer vision and pattern recognition. p 4700\u20138","DOI":"10.1109\/CVPR.2017.243"},{"key":"5305_CR53","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. Proceedings of the IEEE conference on computer vision and pattern recognition. p 2818\u201326","DOI":"10.1109\/CVPR.2016.308"},{"key":"5305_CR54","doi-asserted-by":"crossref","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, Alemi AA (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. Thirty-first AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"5305_CR55","doi-asserted-by":"crossref","unstructured":"Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05): Ieee. p 886\u201393","DOI":"10.1109\/CVPR.2005.177"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05305-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-05305-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05305-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,3]],"date-time":"2024-04-03T13:18:53Z","timestamp":1712150333000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-05305-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2]]},"references-count":55,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["5305"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-05305-4","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2]]},"assertion":[{"value":"30 January 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 March 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}