The study was led by Hyung-Ju Na, a wound, ostomy, and continence nurse at Seoul Metropolitan Dongbu Hospital, together with Sung-Hee Yoo of the College of Nursing at Chonnam National University. Rather than simply cataloging gaps in practice, the researchers anchored their investigation in the Information–Motivation–Behavioral Skills model, or IMB model, a framework originally developed to explain health behavior change and later adapted to clinical performance. The model proposes that behavior is driven by three interacting ingredients: accurate information, motivation to act, and the behavioral skills needed to translate intention into action. In this study, those ingredients were mapped onto pressure injury care knowledge, attitudes and institutional support, and self-efficacy, respectively.

Pressure injuries develop when sustained pressure, often combined with shear and moisture, compromises blood flow to the skin and underlying tissue, particularly over bony prominences such as the sacrum, heels, and hips. In frail, elderly residents who may be immobile, incontinent, or malnourished, the risk escalates dramatically. Once a deep injury forms, it can take months to heal, exposes the patient to infection and pain, and signals a breakdown in preventive care. That is why pressure injury management is widely treated as a critical indicator of nursing quality in long-term care facilities, and why understanding its determinants carries weight well beyond the ward.

To probe those determinants, the team conducted a descriptive, cross-sectional survey of 200 nurses working at five long-term care facilities in Seoul. Data were collected between June 1 and December 1, 2020, using a self-administered questionnaire that measured four domains: knowledge of pressure injury care, attitudes and perceived institutional support, self-efficacy, and actual performance of evidence-based pressure injury care. The researchers then used hierarchical stepwise regression, a statistical technique that adds predictor variables in stages to see how much additional explanatory power each contributes, while first accounting for the nurses’ general characteristics such as age, experience, and education level.

The headline finding was sobering but instructive. The mean item score for evidence-based pressure injury care performance was 3.89 out of 5, with a standard deviation of 0.61. In other words, nurses in these facilities were performing reasonably well but not at the near-perfect level that evidence-based prevention ideally demands. The regression analysis then identified four significant factors, ranked by the strength of their influence. Knowledge of pressure injury care came first, with a standardized beta coefficient of 0.38 and a p-value below 0.001. Self-efficacy followed at 0.23 (p = 0.001), then the provision of annual pressure injury care education at 0.22 (p = 0.003), and finally education level at 0.17 (p = 0.003). Together, these variables explained 38.3 percent of the total variance in performance, a substantial share for a complex clinical behavior measured in a real-world setting.

The prominence of knowledge is perhaps the most striking result. In the IMB framework, information is the foundation upon which motivation and skills build, and the data here bear that out: nurses who scored higher on pressure injury knowledge were markedly more likely to perform evidence-based care. This matters because clinical knowledge is not static. Pressure injury prevention guidelines evolve, covering topics such as risk assessment tools, repositioning schedules, support surfaces, skin moisture management, and nutrition. Nurses whose training predates current recommendations may be applying outdated techniques without realizing it, which makes periodic knowledge updating a genuine safety issue rather than an administrative box-tick.

Yet knowledge alone did not carry the story. Self-efficacy, the confidence a nurse has in her own ability to execute the required behaviors, emerged as the second strongest predictor. This aligns with a large body of behavioral science showing that people who believe they can perform an action are more likely to persist at it, especially when the action is effortful or the environment is challenging. Preventing pressure injuries in a busy long-term care ward is exactly that kind of effortful behavior: it requires vigilance, coordination with aides, advocacy for equipment, and repeated reassessment. A nurse who knows what to do but doubts her capacity to do it amid competing demands may quietly let best practice slip.

The third significant factor, the provision of annual pressure injury care education, shifts the lens from the individual to the organization. This is a notable finding because it demonstrates that institutional support contributes to performance independently of nurses’ personal educational backgrounds. A facility that commits to yearly training is, in effect, doing two things at once: refreshing the knowledge base of its staff and signaling that pressure injury prevention is a priority worth the organization’s time and resources. The authors conclude that enhancing care quality requires systematic, multi-level interventions that focus on updating clinical knowledge and bolstering nurses’ self-efficacy through sustained organizational commitment, not one-off lectures or posters in the break room.

Even the weakest predictor in the model, education level, carries practical implications. Its significance suggests that nurses with higher formal qualifications tend to perform better evidence-based care, but the fact that it ranked last—behind knowledge, self-efficacy, and annual training—implies that targeted continuing education can compensate for differences in academic credentials. For long-term care facilities, which often struggle to recruit highly credentialed nurses, this is an encouraging message: investment in structured, recurring in-service education may narrow the gap that degrees alone cannot.

The study does have boundaries worth noting. It was cross-sectional, capturing a single moment in time, so it can identify associations but cannot prove that boosting knowledge or self-efficacy causes better performance. The sample comprised 200 nurses from five facilities in a single city, and data were self-reported, which introduces the possibility of response bias. The researchers also note that the work received no specific grant from any funding agency, and the study was approved by the Institutional Review Board of Chonnam National University with written informed consent from all participants, conducted in accordance with the Declaration of Helsinki. Still, by grounding the analysis in a tested behavioral model and quantifying the relative weight of each factor, the study offers long-term care administrators a concrete roadmap: audit and refresh staff knowledge, build confidence through skills-based training and supportive supervision, and institutionalize annual pressure injury education. In a field where a single deep wound can mean months of suffering for a vulnerable resident, those levers may prove among the most cost-effective tools available.

Subject of Research: Determinants of evidence-based pressure injury care performance among nurses in long-term care facilities using the Information–Motivation–Behavioral Skills model

Article Title: Factors associated with evidence-based pressure injury care among nurses in long-term care facilities: an information–motivation–behavioral skills model-based study

Article References: Na, H.-J., & Yoo, S.-H. (2026). Factors associated with evidence-based pressure injury care among nurses in long-term care facilities: an information–motivation–behavioral skills model-based study. BMC Nursing. https://doi.org/10.1186/s12912-026-05472-4

Image Credits: AI Generated

DOI: 10.1186/s12912-026-05472-4

Keywords: pressure injury, bedsores, long-term care facilities, nursing, evidence-based practice, IMB model, self-efficacy, nurse education, cross-sectional study, South Korea, wound care, nursing quality

The study, led by Wenyu Cheng and colleagues at the First Hospital of Shanxi Medical University in Taiyuan, was published in BMC Nursing. Rather than treating AI literacy as a single score that rises or falls from nurse to nurse, the team used a statistical technique called latent profile analysis, which searches for hidden subgroups within a population whose responses share a common pattern. This person-centered approach is designed to capture heterogeneity that averages conceal, and it revealed a workforce far more stratified than conventional surveys suggest.

Artificial intelligence literacy, as the researchers frame it, is a multidimensional construct. It encompasses awareness of what AI systems can and cannot do, the practical ability to apply them, the capacity to evaluate their outputs critically, and an understanding of the ethical questions that arise when algorithms touch patient care. A nurse might score high on one dimension and low on another, and the combination matters: a clinician who can operate a predictive early-warning dashboard but cannot question its recommendations occupies a very different professional position from one who combines hands-on fluency with ethical vigilance.

When the latent profile analysis was run, four patterns emerged. The first and smallest group was labeled the actively embracing type, nurses who scored consistently high across awareness, application, evaluation, and ethics. The second, the knowledge-application dissociation type, describes nurses who understand AI conceptually but fail to translate that knowledge into practice. The third, the skill-oriented type, shows the reverse configuration: solid procedural competence paired with comparatively weaker grasp of underlying knowledge and critical evaluation. The fourth and most concerning group, the weak literacy type, scores low across the board. Together, the middle two categories accounted for the majority of the 1,160 participants, while the actively embracing type represented the smallest share of the sample.

The technical distinction between these profiles is more than academic taxonomy. In a hospital setting, the knowledge-application dissociation profile implies a training pipeline that successfully delivers theory, through lectures, policy documents, or awareness campaigns, but stops short of supervised, hands-on engagement with clinical AI tools. The skill-oriented profile suggests the opposite failure mode: nurses learn button-level operation of specific systems without developing the conceptual foundation that would let them generalize to new tools, spot errors, or reason about algorithmic bias. Both patterns represent half-built literacy, and each demands a different corrective intervention.

To understand what pushes nurses into one profile or another, the team turned to multivariate logistic regression, a method that estimates the odds of belonging to a given profile while adjusting for other variables simultaneously. Six factors emerged as significantly associated with profile membership: age, education level, prior AI training experience, frequency of AI use at work, attitudes toward AI, and perceived organizational support. Nurses holding a bachelor’s degree or higher were more likely to occupy the stronger profiles, as were younger respondents, though the relationship with age is not simply a matter of digital nativity, since it interacts with career stage and exposure to continuing education.

Two of the associated factors stand out for their practical implications. The first is training experience: nurses who had participated in any structured AI training were far more likely to land in the higher-literacy profiles, which makes targeted training for those without such experience an obvious priority. The second is frequency of use, which points to a self-reinforcing loop. Nurses who encounter AI tools regularly build familiarity and confidence, which in turn encourages further use, while those working on units without deployed AI systems have few opportunities to practice and risk falling permanently behind. The study’s findings suggest that access itself, not merely motivation, may be a bottleneck for literacy development.

The psychological and organizational dimensions proved equally consequential. Using validated instruments to measure attitudes toward AI in the workplace and organizational support, the researchers found that nurses with more positive attitudes and stronger institutional backing were significantly more likely to belong to the high-literacy profiles. This aligns with a broader body of technology-acceptance research showing that perceived usefulness and perceived organizational backing are powerful predictors of adoption behavior. In practical terms, a hospital that communicates a clear AI strategy, provides time and resources for learning, and frames AI as augmentation rather than surveillance is likely to cultivate a more literate workforce than one that simply installs software and expects adaptation.

The authors flag two groups for special attention: nurses with lower educational attainment and those without prior AI training, who they note may encounter greater obstacles in developing AI literacy. This matters for equity as much as for efficiency. If AI competence accrues disproportionately to the already advantaged, hospitals risk creating a two-tier clinical workforce in which some nurses can leverage intelligent decision support while others are locked out of it, with consequences for career progression and potentially for patient safety. Deliberately designed bridging programs, delivered at the bedside and tailored to different starting points, could interrupt that divergence before it hardens.

The study carries the usual caveats of its design. As a cross-sectional survey conducted between January and April 2026, it captures a snapshot rather than a trajectory, so the causal direction of the associations cannot be established with certainty; it is possible, for instance, that positive attitudes drive AI use, or that successful use breeds positive attitudes, or both. The sample, while large and drawn from multiple centers, is confined to China’s hospital system, and the translation of AI literacy profiles to other health systems with different technology infrastructures and nursing education pathways remains an open question. Nevertheless, the methodological shift from averaging nurses to sorting them is the study’s real contribution, and it is one likely to be imitated. As AI penetrates triage, monitoring, documentation, and diagnosis, health systems worldwide will need to know not just how much AI literacy their nurses have, but which kinds, in whom, and under what conditions it grows. This study offers one of the first rigorous maps of that landscape, and its message is clear: the AI-ready nurse is not yet the norm, but the factors that produce one, training, practice, attitude, and institutional support, are all within reach of deliberate policy.

Subject of Research: Artificial intelligence literacy profiles and associated factors among clinical nurses in China

Article Title: Potential categories and associated factors of artificial intelligence literacy in Chinese nurses: a multicenter cross-sectional study

Article References: Potential categories and associated factors of artificial intelligence literacy in Chinese nurses: a multicenter cross-sectional study. (n.d.). https://doi.org/10.1186/s12912-026-05406-0

Image Credits: AI Generated

DOI: 10.1186/s12912-026-05406-0

Keywords: artificial intelligence literacy, nursing, latent profile analysis, cross-sectional study, digital health, clinical decision support, health workforce, organizational support, AI training, China, technology acceptance, nurse education