RESOURCES

The P‐CaReSS

The 18-item P-CaReSS includes three types of parent behaviors: verbal, nonverbal and emotional behaviors. These parent interaction behaviors comprise five caring domains – knowing, being with, doing for, enabling, and maintaining belief – and one noncaring domain.

Bai, J., Swanson, K., Harper, F. W. K., Penner, L. A., & Santacroce, S. J. (2018). Parent Caring Response Scoring System: development and psychometric evaluation in the context of childhood cancer-related port starts. Scandinavian journal of caring sciences, 32(2), 734–745. https://doi.org/10.1111/scs.12504

Bai, J., Swanson, K. M., Harper, F. W. K., Santacroce, S. J., & Penner, L. A. (2018). Longitudinal Analysis of Parent Communication Behaviors and Child Distress during Cancer Port Start Procedures. Pain management nursing : official journal of the American Society of Pain Management Nurses, 19(5), 487–496. https://doi.org/10.1016/j.pmn.2018.01.002

Bai, J., Harper, F. W. K., Penner, L. A., Swanson, K., & Santacroce, S. J. (2017). Parents’ Verbal and Nonverbal Caring Behaviors and Child Distress During Cancer-Related Port Access Procedures: A Time-Window Sequential Analysis. Oncology nursing forum, 44(6), 675–687. https://doi.org/10.1188/17.ONF.675-687

The Readiness for Hospital Discharge Scale (RHDS)−Parent Form

The Chinese version of RHDS (C-RHDS)−Parent Form included 22 items with 4 subscales, accounting for 56.71% of the total variance. The C-RHDS−Parent Form and its subscales showed good reliability (Cronbach’s α values 0.78–0.92).

Chen, Y., & Bai, J. (2017). Reliability and validity of the Chinese version of the Readiness for Hospital Discharge Scale-Parent Form in parents of preterm infants. International journal of nursing sciences, 4(2), 88–93. https://doi.org/10.1016/j.ijnss.2017.01.009

Chen, Y., Zhang, J., & Bai, J. (2016). Effect of an educational intervention on parental readiness for premature infant discharge from the neonatal intensive care units. Journal of advanced nursing, 72(1), 135–146. https://doi.org/10.1111/jan.12817

The COMFORT-Behavior Scale – Chinese Version

The FLACC and COMFORT-BChinese scales were both found to be useful tools for pain detection in this vulnerable population, similarly to previous studies of children <7 years old with critical illnesses. Both scales showed excellent sensitivity and specificity based on VASobs to predict pain during the postcardiac surgery period.

Bai, J., Hsu, L., Tang, Y., & van Dijk, M. (2012). Validation of the COMFORT Behavior scale and the FLACC scale for pain assessment in Chinese children after cardiac surgery. Pain management nursing : official journal of the American Society of Pain Management Nurses, 13(1), 18–26. https://doi.org/10.1016/j.pmn.2010.07.002

Bai, J., & Hsu, L. (2013). Pain status and sedation level in Chinese children after cardiac surgery: an observational study. Journal of clinical nursing, 22(1-2), 137–147. https://doi.org/10.1111/j.1365-2702.2012.04263.x

The Chinese version of the Essentials of Magnetism II

The C-EOM II is a promising scale to assess the HWE for Chinese ICU nurses.

Bai, J., Zhang, Q., Wang, Y., Yu, L. P., Pei, X. B., Cheng, L., & Hsu, L. (2015). Work environment for Chinese nurses in different types of ICUs: a multisite cross-sectional survey. Journal of nursing management, 23(4), 498–509. https://doi.org/10.1111/jonm.12163

Bai, J., Hsu, L., & Zhang, Q. (2015). Validation of the Essentials of Magnetism II in Chinese critical care settings. Nursing in critical care, 20(3), 134–145. https://doi.org/10.1111/nicc.12041

Bai J. (2016). Does job satisfaction mediate the relationship between healthy work environment and care quality?. Nursing in critical care, 21(1), 18–27. https://doi.org/10.1111/nicc.12122

The QIIME 2 AWS Pipeline

Using AWS features, we developed a microbiome data analysis pipeline that included Amazon Simple Storage Service for microbiome sequence storage, Linux Elastic Compute Cloud (EC2) instances (ie, servers) for data computation and analysis, and security keys to create and manage the use of encryption for the pipeline.

Bai, J., Jhaney, I., & Wells, J. (2019). Developing a Reproducible Microbiome Data Analysis Pipeline Using the Amazon Web Services Cloud for a Cancer Research Group: Proof-of-Concept Study. JMIR medical informatics, 7(4), e14667. https://doi.org/10.2196/14667

Bai, J., Jhaney, I., Daniel, G., & Watkins Bruner, D. (2019). Pilot Study of Vaginal Microbiome Using QIIME 2™ in Women With Gynecologic Cancer Before and After Radiation Therapy. Oncology nursing forum, 46(2), E48–E59. https://doi.org/10.1188/19.ONF.E48-E59