Check health professions papers: citations, claims and retractions

Biological variation at long-term renal post-transplantation

Biosca, C., Ricós, C., Lauzurica, R., & Petersen, P. H. (2006) · Clinica Chimica Acta

Clinical Laboratory Practices and Quality ControlRenal Transplantation Outcomes and Treatments

Cited by 9

Factores predictores de conductas promotoras de salud en docentes de enfermería de tres universidades de Cali, Colombia

Triviño-Vargas, Z. G., & López-Hurtado, M. X. (2018) · Universidad y Salud

Health and Wellbeing ResearchHealth and Lifestyle Studies

Cited by 9

Caregiving and role conflict distress

MacKenzie, J. (2024) · Clinical Ethics

Ethics in medical practiceEthics and bioethics in healthcare

Cited by 9

Machine learning for the prediction of atherosclerotic cardiovascular disease during 3‐year follow up in Chinese type 2 diabetes mellitus patients

Ding, J., Luo, Y., Shi, H., Chen, R., Luo, S., Yang, X., Xiao, Z., Liang, B., Yan, Q., Xu, J., et al. (2023) · Journal of Diabetes Investigation

Artificial Intelligence in HealthcareMachine Learning in Healthcare

Cited by 9

Communication as an ecological system.

Borg, E., Bergkvist, C., Olsson, I.-S., Wikström, C., & Borg, B. (2008) · International Journal of Audiology

Hearing Loss and RehabilitationHearing Impairment and Communication

Cited by 9

Perception of Physical Demand, Mental Demand, and Performance: A Comparison of Two Voice Interventions for Parkinson's Disease

Richardson, K., Huber, J. E., Kiefer, B., & Snyder, S. (2022) · American Journal of Speech-Language Pathology

Voice and Speech DisordersDysphagia Assessment and Management

Cited by 9

Validation of the Arabic Version of the Maslach Burnout Inventory-HSS Among Tunisian Medical Residents (A-MBI-MR): Factor Structure, Construct Validity, Reliability, and Gender Invariance

Henchiri, H., Tannoubi, A., Harrathi, C., Boussayala, G., Quansah, F., Hagan, J. E., Mechergui, H., Chaabeni, A., Chebbi, T., Lakhal, T. B., et al. (2025) · Healthcare

Healthcare professionals’ stress and burnoutStress and Burnout Research

Cited by 9

Activity participation and perceived health status in patients with severe mental illness: a prospective study

Ng, S. S., Leung, T. K., Ng, P. P., Ng, R. K., & Wong, A. T. (2020) · East Asian Archives of Psychiatry

Occupational Therapy Practice and ResearchSchizophrenia research and treatment

Cited by 9

Accuracy of acoustic evaluation of swallowing as a diagnostic method of dysphagia in individuals affected by stroke: preliminary analysis

Enz, V. C. Q., Vaz, A. R. C., Nunes, M. C. D. A., Rosa, M. D. O., Nunes, J. D. A., Marques, J. M., & Santos, R. S. (2022) · Dysphagia

Dysphagia Assessment and ManagementVoice and Speech Disorders

Cited by 9

Digitally based blood pressure self-monitoring program that promotes hypertension self-management and health education among patients with low-income: usability study

Poblete, J. Y., Vawter, N. L., Lewis, S. V., Felisme, E. M., Mohn, P. A., Shea, J., Northrup, A. W., Liu, J., Al-Rousan, T., & Godino, J. G. (2023) · JMIR Human Factors

Mobile Health and mHealth ApplicationsBlood Pressure and Hypertension Studies

Cited by 9

Learning from community-led and co-designed m-health interventions

Duncan, M. J., & Kolt, G. S. (2019) · The Lancet Digital Health

Mobile Health and mHealth ApplicationsDigital Mental Health Interventions

Cited by 9

The Potential of a Digital Weight Management Program to Support Specialist Weight Management Services in the UK National Health Service: Retrospective Analysis

Richards, R., Wren, G., & Whitman, M. (2024) · JMIR Diabetes

Mobile Health and mHealth ApplicationsObesity, Physical Activity, Diet

Cited by 9

Communicative Accessibility in Aphasia: An Investigation of the Interactional Context of Long-Term Care Facilities

Azios, J. H., Damico, J. S., & Roussel, N. (2018) · American Journal of Speech-Language Pathology

Assistive Technology in Communication and MobilityDementia and Cognitive Impairment Research

Cited by 9

Integration of RFID and Computer Vision for Remote Object Perception for Individuals Who Are Blind

McDaniel, T. L., Kahol, K., Villanueva, D., & Panchanathan, S. (2008) · Proceedings of the First International Conference on Ambient Media and Systems

Assistive Technology in Communication and MobilityRFID technology advancements

Cited by 9

Practicing DNPs’ Perceptions of the DNP

Stoeckel, P., & Kruschke, C. (2013) · Clinical Scholars Review

Nursing Roles and PracticesNursing Education, Practice, and Leadership

Cited by 9

Do Published Studies of Educational Outreach Provide Documentation of Potentially Important Characteristics?

Van Hoof, T. J., Miller, N. E., & Meehan, T. P. (2013) · American Journal of Medical Quality

Health Sciences Research and EducationHealth Policy Implementation Science

Cited by 9

Understanding the relationship between care team perceptions about CHWs and CHW integration within a US health system, a qualitative descriptive multiple embedded case study

McCarville, E. E., Martin, M. A., Pratap, P. L., Pinsker, E., Seweryn, S. M., & Peters, K. E. (2022) · BMC Health Services Research

Nursing Roles and PracticesInterprofessional Education and Collaboration

Cited by 9

Age-specific differences in patient reported outcomes among adults with atherosclerotic cardiovascular disease: Medical expenditure panel survey 2006–2015

Okunrintemi, V., Benson, E.-M. A., Derbal, O., Miedema, M. D., Blumenthal, R. S., Tibuakuu, M., Ogunmoroti, O., Khan, S. U., Mamas, M. A., Gulati, M., et al. (2020) · American Journal of Preventive Cardiology

Patient-Provider Communication in HealthcarePatient Satisfaction in Healthcare

Cited by 9

Identifying Metabolic Syndrome Easily and Cost Effectively Using Non-Invasive Methods with Machine Learning Models

Xu, W., Zhang, Z., Hu, K., Fang, P., Li, R., Kong, D., Xuan, M., Yue, Y., She, D., & Xue, Y. (2023) · Diabetes, Metabolic Syndrome and Obesity

Artificial Intelligence in HealthcareDiabetes, Cardiovascular Risks, and Lipoproteins

Cited by 9

Accuracy of Speech Sound Analysis: Comparison of an Automatic Artificial Intelligence Algorithm With Clinician Assessment

Carl, M., Rudyk, E., Shapira, Y., Rusiewicz, H. L., & Icht, M. (2024) · Journal of Speech, Language, and Hearing Research

Voice and Speech DisordersLanguage Development and Disorders

Cited by 9

Machine learning model for umbilical cord classification using combination coiling index and texture feature based on 2-D Doppler ultrasound images

Pradipta, G. A., Wardoyo, R., Musdholifah, A., & Sanjaya, I. N. H. (2022) · Health Informatics Journal

Artificial Intelligence in HealthcareRadiomics and Machine Learning in Medical Imaging

Cited by 9

Estimating testosterone concentrations in adolescent girls: Comparison of two direct immunoassays to liquid chromatography-tandem mass spectrometry

McCartney, C. R., Burt Solorzano, C. M., Patrie, J. T., Marshall, J. C., & Haisenleder, D. J. (2018) · Steroids

Clinical Laboratory Practices and Quality ControlHormonal and reproductive studies

Cited by 9

Predictors before and after multimodal rehabilitation for pain acceptance and engagement in activities at a 1-year follow-up for patients with whiplash-associated disorders (WAD)—a study based on the Swedish Quality Registry for Pain Rehabilitation (SQRP)

Söderlund, A., Löfgren, M., & Stålnacke, B.-M. (2018) · The Spine Journal

Musculoskeletal pain and rehabilitationMedical Practices and Rehabilitation

Cited by 9

Time Is of the Essence: Achieving Prompt and Effective Antimicrobial Therapy of Bloodstream Infection With Advanced Hospital Information Systems

Morquin, D., Lejeune, J., Agostini, C., Godreuil, S., Reynes, J., Le Moing, V., & Lamy, B. (2024) · Clinical Infectious Diseases

Electronic Health Records SystemsBacterial Identification and Susceptibility Testing

Cited by 9