Patterns of information literacy and their predictors among emergency department nurses: a latent profile analysis based on the person-context interaction theory

Emergency department crowding: An assessment of the potential impact of the See-and-Treat protocol for patient flow management at an Italian hospital

BACKGROUND

Crowding and patient flow management are among the most relevant issues for emergency departments (EDs). This results in delayed treatment, adverse outcomes and increased costs. For these reasons, nurse-independent treatment protocols were developed aimed at managing non-emergency patients outside EDs thus improving patient flow. Our objective was to assess the potential impact of the implementation of the "See-and-Treat" protocol on eligible patients and related healthcare costs at an Italian ED.

METHODS

We selected all minor access codes from 2022 at the ED of Sassuolo Hospital in Northern Italy. We only included subjects discharged to home, while we excluded those who required specialized medical care or had received "Fast-Track" treatment. We identify a list of medical conditions to identify subjects eligible for inclusion in the See-and-Treat protocol and calculate the related healthcare costs.

RESULTS

Of 40,906 individual ED admissions, 2,607 (6.4%) qualified for See-and-Treat management. Limb injuries and pain were the leading conditions at presentation. Through cost analysis, we found that implementation of the See-and-Treat protocol may result in savings over €100,000/year at Sassuolo Hospital, and over €7 million if projected to the entire Emilia-Romagna Region.

CONCLUSIONS

Despite some limitations affecting protocol implementation, especially availability of highly-specialized and specifically-trained nurses, the study provided a foundation for a more comprehensive understanding of the implementation of the See-and-Treat protocol as a possible valid model with a view to both human and economic healthcare resources.

Latent profiles of multi-dimensional sleep characteristics and association with overweight/obesity in Chinese preschool children

OBJECTIVES

To examine the association between latent profiles of multi-dimensional sleep characteristics and overweight/obesity (OWO) in Chinese preschool children.

STUDY DESIGN

The cross-sectional analysis included 3204 preschool children recruited from 24 kindergartens in Shanghai. Parents reported children's demographics and sleep characteristics, including sleep duration, timing and disturbances. Latent profile analysis (LPA) was used to identify sleep subtypes. Logistic regression models were used to evaluate the associations between sleep characteristics/subtypes and OWO.

RESULTS

Short sleep duration, late bedtime, long social jetlag and sleep disturbances were significantly associated with increased OWO. However, when considering the interplay of sleep duration and timing, there was no significant association between sleep duration and OWO for children sleeping later than 22:00. Three sleep subtypes were identified based on children's sleep duration, timing and disturbances: "Average Sleepers" (n = 2107, 65.8 %), "Good Sleepers" (n = 481, 15.0 %), and "Poor Sleepers" (n = 616, 19.2 %). "Good Sleepers" had reduced odds of being OWO (AOR, 0.72; 95 % CI, 0.56-0.93) compared to "Average Sleepers", while "Poor Sleepers" showed an increased risk of OWO (AOR, 1.36; 95 % CI, 1.11-1.67).

CONCLUSIONS

These findings highlight that improving multiple sleep characteristics simultaneously is a promising option to prevent and intervene childhood obesity.

Influence of information anxiety on core competency of registered nurses: mediating effect of digital health literacy

BACKGROUND

In the information age of health care, nurses often face information overload, leading to negative emotions, e.g., anxiety that may impede the adoption of evidence-based practice and clinical decision-making process. Nurses with higher digital health literacy can effectively process and manage information. Despite this, no research has explored the relationship between information anxiety, digital health literacy, and core competency among nurses. Therefore, this study aims to investigate the mediating effects of digital health literacy on information anxiety and core competency among nurses.

METHODS

From July to October 2023, the data for this cross-sectional study were collected. The study surveyed a total of 608 nurses from three tertiary hospitals in Fujian Province, and the survey instruments included a sociodemographic information questionnaire, Chinese revision version of the Digital Health Literacy Instrument (CR-DHLI), Information Anxiety Scale (IAS), and Competency Inventory for Registered Nurses (CIRN). Descriptive statistics and Pearson correlation analysis were conducted using SPSS 29.0, and the mediating effect of digital health literacy was examined using Mplus.

RESULTS

The mean score of nurses' information anxiety, digital health literacy, and core competency was 3.03 ± 0.91, 2.46 ± 0.56, 2.72 ± 0.88, respectively. And the mediation model of information anxiety on core competency for nurses showed a good model fit index (χ²/df = 2.207, CFI = 0.985, TLI = 0.982, RMSEA = 0.045, SRMR = 0.035). Digital health literacy was positively correlated with nurses' core competency but negatively correlated with information anxiety. The results of path analysis revealed that information anxiety had negative and significant direct effects on NCC (β = -0.119, P = 0.004) and DHL (β = -0.297, P < 0.001). DHL had a positive effect on NCC (β = 0.306, P < 0.001). Digital health literacy played a partial mediating role, accounting for 43.54% of the relationship between information anxiety and nurses' core competency.

CONCLUSIONS

Information anxiety among nurses was at relatively high levels, which had a negative impact on the core competency of nurses. This issue requires attention from nursing managers. The mediating role of digital health literacy in the relationship between information anxiety and core competency among nurses has been established. Nursing managers should strengthen the evaluation of nurses' DHL and devise effective support strategies to enhance DHL, thus improving the core competence of nurses in information age.

Professional competencies in geriatric nursing for geriatric nurses: a latent profile analysis

BACKGROUND

As the global population continues to age, social realities such as advanced age, disability and living alone are coming to the fore, and the demand for medical care and health services for the elderly is increasing dramatically, especially in geriatrics. Given the important role geriatric nurses play in the diagnosis and treatment of diseases and rehabilitation of elderly patients, and due to the uniqueness and complexity of geriatric work, this requires geriatric nurses not only to have the competencies that are available in general nursing, but also to ensure that they have sufficient geriatric core competencies in order to effectively meet the needs of the patients and accelerate their recovery. Although previous studies have investigated the core competencies of nursing staff, there has been little research on geriatric nurses' core geriatric nursing competencies and their predictors. The aim of this study was to investigate the current status of the geriatric nursing competency inventory (GNCI) among geriatric nurses using latent profiling, to identify potential subgroups and their population characteristics, and to explore the factors that influence the potential subgroups.

METHODS

From January to March 2024, 1,313 geriatric nurses in Hefei City were selected by stratified cluster sampling method and surveyed with general information questionnaire, geriatric nursing competency inventory, and occupational coping self-efficacy scale for nurses(OCSE-N). Potential subgroups of GNCI differences among geriatric nurses were identified by latent profile analysis (LPA). Multiple logistic regression analyses were used to explore the factors influencing the GNCI of geriatric nurses with different latent profiles.

RESULTS

Geriatric nurses' OCSE-N was positively correlated with GNCI, and the GNCI score was 123.06(41.60), which indicated that geriatric nurses' GNCI was at an intermediate level. The OCSE-N score was 35.44(7.34), which was at a relatively high level. There was heterogeneity in the GNCI of geriatric nurses, which was classified into three subgroups i.e., Low-competency group, Medium-competency group, High-competency group. The results of multiple logistic regression analyses showed that OCSE-N, title, whether or not they attended geriatric nurse specialist training, and specialist nurse status were predictors of GNCI among geriatric nurses (P < 0.05).

CONCLUSION

The GNCI categorical characteristics of geriatric nurses are obvious, and nursing managers should adopt targeted interventions according to the characteristics of nurses in different profiles to improve the overall quality of care.

Exploring the influencing factors of patient safety competency of clinical nurses: a cross-sectional study based on latent profile analysis

BACKGROUND

Clinical nurses play an important role in ensuring patient safety. Nurses' work experience, organizational environment, psychological cognition, and behavior can all lead to patient safety issues. Improving nurses' attention to patient safety issues and enhancing their competence in dealing with complex medical safety issues can help avoid preventable nursing adverse events. Therefore, it is necessary to actively identify the latent profiles of patient safety competency of clinical nurses and to explore the influencing factors.

METHODS

A cross-sectional design was conducted. A total of 782 Chinese registered nurses were included in the study. Demographic characteristics questionnaire, Error Management Climate scale, Security Questionnaire, Proactive Behavior Performance scale and Patient Safety Competency Self-Rating Scale of Nurses were used. Latent profile analysis (LPA) was performed to categorize nurses into latent subgroups with patient safety competency differences. Multinomial logistic regression was conducted to explore the influencing factors of nurses' patient safety competency (PSC) in different latent profiles.

RESULTS

A total of 782 questionnaires were valid. Nurses' PSC was positively related to error management climate, and psychological safety and proactive behavior. The PSC score was 121.31 (SD = 19.51), showing that the PSC of clinical nurses was at the level of the medium on the high side. The error management climate score was 70.28 (SD = 11.93), which was at a relatively high level. The psychological safety score was 61.21 (SD = 13.44), indicating a moderate to low level. The proactive behavior score was 37.60 (SD = 7.33), which was at a high level. The latent profile analysis result showed that three groups of profile models were fitted acceding to the evaluation of PSC. They were defined as Low-competency Group (74 (9.5%)), Medium-competency Group (378 (48.3%)) and High-competency Group (330 (42.2%). Working years, professional titles, departments, error management climate, psychological security and proactive behavior were the influencing factors of PSC in three latent profiles.

CONCLUSIONS

The PSC of clinical nurses had obvious classification characteristics, and the main influencing factors were working years, professional titles, working departments, error management climate, psychological security and proactive behavior. This study suggests that managers should pay attention to the continuous cultivation of patient safety competence among clinical nurses, provide targeted intervention measures for nurses at different work stages, professional titles, and departments, and use efficient management strategies to create a positive error management atmosphere. In patient safety management, providing nurses with more psychological security is conducive to stimulating more proactive behaviors and continuously improving the level of patient safety competence.