Community Health Nurses’ Perceptions of Emerging Artificial Intelligence and its Impact on Nursing Practice: An Explanatory Sequential Mixed Methods Study
- 1School of Nursing, University of Northern British Columbia, Prince George, British Columbia, Canada
- 2School of Nursing, University of British Columbia, Vancouver, British Columbia, Canada
- 3Department of Computing Science, Thompson Rivers University, Kamloops, British Columbia, Canada
Abstract
Background: Artificial intelligence (AI) continues to emerge into nursing practice with much of the AI research being conducted in the acute care sector. Community health nurses (CHNs) have distinct skills and knowledge focusing on helping clients to live well in the community. Community practice is an essential part of healthcare yet often overlooked, creating a knowledge gap. This research aims to understand CHNs’ knowledge and perceptions of AI to inform future practice.
Method: An explanatory sequential mixed methods design was conducted, a cross-sectional survey followed by focus groups to combine both sets of results for a comprehensive perspective.
Results: There were 228 survey respondents and 27 participants forming 8 focus groups. The survey revealed professional concerns: AI giving a wrong recommendation (77.7%) or if a correct recommendation was dismissed (73.8%) with focus groups explaining they knew their responsibility in decision-making, their concerns focused on accepting AI recommendations blindly or using recommendations to assist with decision-making. Overall CHNs felt AI applications had usefulness (68.8%-88%), the focus groups further explained clinical relevance, user friendly and time efficiency were factors that would determine usefulness.
Conclusions: Dedicated time for AI education for CHNs is needed to address how recommendations are generated and the significance to give to AI recommendations. Clear policies and guidelines need to be established to inform CHNs use of AI. Success of AI in CHNs practice is dependent on applications that do not add time to their busy schedules.
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BibTeX
@article{betkus2026community,
title = {{Community Health Nurses’ Perceptions of Emerging Artificial Intelligence and its Impact on Nursing Practice: An Explanatory Sequential Mixed Methods Study}},
author = {Mary Henderson Betkus and Davina Banner and Leanne M. Currie and Piper Jackson and Shannon Freeman},
journal = {The Canadian Journal of Nursing Research},
year = {2026},
month = jul,
doi = {10.1177/08445621261463882},
url = {https://doi.org/10.1177/08445621261463882},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - Community Health Nurses’ Perceptions of Emerging Artificial Intelligence and its Impact on Nursing Practice: An Explanatory Sequential Mixed Methods Study
AU - Mary Henderson Betkus
AU - Davina Banner
AU - Leanne M. Currie
AU - Piper Jackson
AU - Shannon Freeman
PY - 2026
DA - 2026/07/10
JO - The Canadian Journal of Nursing Research
DO - 10.1177/08445621261463882
UR - https://doi.org/10.1177/08445621261463882
AB - Background: Artificial intelligence (AI) continues to emerge into nursing practice with much of the AI research being conducted in the acute care sector. Community health nurses (CHNs) have distinct skills and knowledge focusing on helping clients to live well in the community. Community practice is an essential part of healthcare yet often overlooked, creating a knowledge gap. This research aims to understand CHNs’ knowledge and perceptions of AI to inform future practice. Method: An explanatory sequential mixed methods design was conducted, a cross-sectional survey followed by focus groups to combine both sets of results for a comprehensive perspective. Results: There were 228 survey respondents and 27 participants forming 8 focus groups. The survey revealed professional concerns: AI giving a wrong recommendation (77.7%) or if a correct recommendation was dismissed (73.8%) with focus groups explaining they knew their responsibility in decision-making, their concerns focused on accepting AI recommendations blindly or using recommendations to assist with decision-making. Overall CHNs felt AI applications had usefulness (68.8%-88%), the focus groups further explained clinical relevance, user friendly and time efficiency were factors that would determine usefulness. Conclusions: Dedicated time for AI education for CHNs is needed to address how recommendations are generated and the significance to give to AI recommendations. Clear policies and guidelines need to be established to inform CHNs use of AI. Success of AI in CHNs practice is dependent on applications that do not add time to their busy schedules.
SN - 0844-5621
SN - 1705-7051
ER -