Over-prescription of antibiotics is prevalent all over the world, contributing to the development of antibiotic resistance. The importance of understanding how physicians prescribe antibiotics is increasingly highlighted for the purpose of promoting good practice. This study aimed to identify factors that shape the antibiotic prescribing behaviors of physicians in primary care based on the theory of planned behavior (TPB). Data were collected from 503 prescribers within 65 primary care facilities in Hubei, tapping into four behavioral aspects leading to antibiotic prescribing based on TPB, namely, attitudes (the degree to which a prescriber is in favor of the use of antibiotics), subjective norms (perceived social pressure to which a prescriber is subject in relation to antibiotic prescriptions), perceived control of behaviors (how easy a prescriber feels in making a rational decision on antibiotic prescriptions) and intentions (the degree to which a prescriber is willing to prescribe antibiotics). A total of 440,268 prescriptions were audited to assess physician antibiotic prescribing practices. The four behavioral constructs were further linked with physician’s actual use of antibiotics using structural equation modelling (SEM) based on TPB. On average, 40.54% (SD = 20.82%) of the outpatient encounters resulted in a prescription for an antibiotic given by the participants and 9.81% (SD = 10.18%) of the patients were given two or more antibiotics. The participants showing a more favorable attitude toward antibiotics had a higher intention to prescribe antibiotics (β = 0.226, p < 0.001) and a lower intention to reduce antibiotic use (β = − 0.211, p < 0.001). Those who perceived lower social pressure (β = 0.113, p = 0.030) and higher control over prescribing behaviors (β = 0.113, p = 0.037) reported a higher intention to reduce the use of antibiotics. However, such intention did not translate into prescribing practice (p > 0.05), although stronger perceived behavioral control was directly linked with lower antibiotic prescriptions (β = − 0.110, p = 0.019). Weaker perceived behavioral control was evident in the participants who showed a less favorable attitude toward antibiotics (β = 0.128, p = 0.001). Antibiotic prescribing practice is not under the volitional control of prescribers in primary care in China. Their rational prescribing practice is likely to be jeopardized by perceived weak control over prescribing behaviors.

Irrational use of antibiotics is a major driver of development of antibiotic resistance, which heavily threatens population health worldwide. Understanding the mechanism of physician’s antibiotic prescribing decisions is increasingly highlighted to promote prudent use of antibiotics. Therefore, the current study aimed to fill the gap, modelling physician’s antibiotic prescribing and identifying the potential intrinsic and external determinants of antibiotic prescribing in primary care. A total of 428,475 prescriptions from 499 prescribers in 65 primary care facilities in Hubei of China were audited. Teixeira Antibiotic Prescribing Behavioral Model (TAPBM) was referred as theoretical basis to identify intrinsic and external predictors of antibiotic prescriptions. A questionnaire survey was conducted, covering potential physician’s intrinsic determinants (knowledge, attitudes and individual characteristics) and external factors both in individual level (patient pressure, time pressure and financial incentives) and institutional level (setting and local socio-economic development). A two-level path analysis was performed linking potential determinants of antibiotic use with physician’s actual practices. About 44.28% of the prescriptions contained antibiotics, with 9.28% containing two or more antibiotics. The multi-level path analysis revealed that knowledge was a significant predictor of attitudes (β = 0.154, p < 0.05), but higher knowledge and attitudes failed to translate into antibiotic prescribing practices ((β = − 0.076 – 0.039, p > 0.05). Instead, external factors played a more important role and physician’s antibiotic use was significantly associated with patient pressure (β = 0.102, p = 0.022), time pressure (β = − 0.164, p = 0.002), financial incentives (β = − 0.133– − 0.155, p = 0.027) and institutional environments (rural area, β = 0.408, p = 0.002; and high socioeconomic setting, β = − 0.641 - -0.578, p < 0.001 ). The prescribers who were male (β = − 0.168, p = 0.007) or had lower qualification (β = − 0.114, p = 0.028) were also more likely to prescribe antibiotics than others. Antibiotic prescribing practices are complex process and associated with both intrinsic (prescriber) and external (patients and institutional environment) factors. A systematic approach is required to curb over-prescription of antibiotics. Apart from educating prescribers, it is equally important, if not more, to educate patients, break incentives and nurture professional culture within organization to reduce the overuse of antibiotics.

Background: The integration of artificial intelligence (AI) in health care has significant potential, yet its acceptance by health care professionals (HCPs) is essential for successful implementation. Understanding HCPs’ perspectives on the explainability and integrability of medical AI is crucial, as these factors influence their willingness to adopt and effectively use such technologies. Objective: This study aims to improve the acceptance and use of medical AI. From a user perspective, it explores HCPs’ understanding of the explainability and integrability of medical AI. Methods: We performed a mixed systematic review by conducting a comprehensive search in the PubMed, Web of Science, Scopus, IEEE Xplore, and ACM Digital Library and arXiv databases for studies published between 2014 and 2024. Studies concerning an explanation or the integrability of medical AI were included. Study quality was assessed using the Joanna Briggs Institute critical appraisal checklist and Mixed Methods Appraisal Tool, with only medium- or high-quality studies included. Qualitative data were analyzed via thematic analysis, while quantitative findings were synthesized narratively. Results: Out of 11,888 records initially retrieved, 22 (0.19%) studies met the inclusion criteria. All selected studies were published from 2020 onward, reflecting the recency and relevance of the topic. The majority (18/22, 82%) originated from high-income countries, and most (17/22, 77%) adopted qualitative methodologies, with the remainder (5/22, 23%) using quantitative or mixed method approaches. From the included studies, a conceptual framework was developed that delineates HCPs’ perceptions of explainability and integrability. Regarding explainability, HCPs predominantly emphasized postprocessing explanations, particularly aspects of local explainability such as feature relevance and case-specific outputs. Visual tools that enhance the explainability of AI decisions (eg, heat maps and feature attribution) were frequently mentioned as important enablers of trust and acceptance. For integrability, key concerns included workflow adaptation, system compatibility with electronic health records, and overall ease of use. These aspects were consistently identified as primary conditions for real-world adoption. Conclusions: To foster wider adoption of AI in clinical settings, future system designs must center on the needs of HCPs. Enhancing post hoc explainability and ensuring seamless integration into existing workflows are critical to building trust and promoting sustained use. The proposed conceptual framework can serve as a practical guide for developers, researchers, and policy makers in aligning AI solutions with frontline user expectations. Trial Registration: PROSPERO CRD420250652253; https://www.crd.york.ac.uk/PROSPERO/view/CRD420250652253

Background: This study aims to explore the impacts of knowledge and attitude on the behavior of antibiotic use during the treatment of the common cold based on the expanding KAP model, and then identify the critical behavioral stage. Methods: A cross-sectional study was conducted on 815 public from 21 community health centers (CHCs) in Chongqing, China. Based on the expanding KAP model, a self-administered questionnaire was designed to measure knowledge, attitude, multi-stage behavior, and perceived threat, in which multi-stage behavior was divided into pre-use antibiotic behavior, during-use antibiotic behavior, and post-use antibiotic behavior. A structural equation model was used to examine the model fit and the direct, indirect, mediating effects, and moderating effect of the variables. Results: The expanding KAP showed good model fit indices with χ²/df = 0.537, RMSEA = 0.033, CFI = 0.973, GFI = 0.971, NFI = 0.934, TLI = 0.979. Knowledge had a positive effect on attitude (β = 0.503, p < 0.05), pre-use antibiotic behavior (β = 0.348, p < 0.05), during-use antibiotic behavior (β = 0.461, p < 0.001), and post-use antibiotic behavior (β = 0.547, p < 0.001). Attitude had a positive effect on during-use antibiotic behavior (β = 0.296, p < 0.001), and post-use antibiotic behavior (β = 0.747, p < 0.001). The mediating effect of attitude was positive among knowledge, during-use antibiotic behavior (β = 0.149, p < 0.05), and post-use antibiotic behavior (β = 0.376, p < 0.001). Perceived threat also had a positive moderating effect between knowledge and post-use antibiotic behavior (β = 0.021, p < 0.001). Conclusions: Knowledge, attitude and perceived threat had different effects on different stages of antibiotic behavior. The critical behavioral stage prioritized the post-use antibiotic behavior and during-use antibiotic behavior over pre-use antibiotic behavior.

Background and Aims: The self-management behavior of patients with diabetes involves a complex set of actions involving medication therapy, lifestyle changes, and management of complications in the daily routine. Our study aims to explore adherence to self-management behaviors by patients with type 2 diabetes and the potential factors influencing those behaviors.Methods: This qualitative study used semi-structured interviews conducted with patients who have type 2 diabetes and who were recruited from the department of endocrinology in a tertiary teaching hospital. Data were analyzed thematically using the interview framework.Results: Overall, 28 patients with type 2 diabetes were recruited and interviewed. Three types of medication noncompliance behaviors were coded. In particular, blindly optimistic attitudes toward the condition in younger patients who had a short duration of diabetes and fear of or pain from medication therapy were key influencing factors. Irregular monitoring and missed follow-up visits were the most frequently mentioned noncompliance behaviors. Poor understanding of blood glucose monitoring, selective ignorance due to pressure of uncontrolled blood glucose, and blindly optimistic attitudes were also identified as key influencing factors. Dietary behaviors were characterized by an overemphasis on the amount of food in the diet and the preference or declination for particular types of food; ignorance of the dietary structure was present. Misconceptions about dietary and exercise practices were the main types of lifestyles’ noncompliance.Conclusion: Our study showed the complex picture of noncompliance with self-management behaviors by patients with type 2 diabetes. Noncompliance covered disordered and arbitrary changes in medication therapy, blood glucose monitoring with poorest adherence, lifestyle modifications and complication management. The study findings identify clear challenges to self-management behavior and identify potential key influencing factors. Future interventions and strategies should aim to help patients translate healthcare provider’s information and instructions into action that improve compliance.

Background: Gestational diabetes mellitus (GDM) threatens GDM mothers and their offspring’s health and breastfeeding is one of the most effective ways to decrease the risk. However, the prevalence of breastfeeding among GDM mothers is far from optimal and how GDM mothers develop their feeding behavior is still unclear. Thus, this study aimed to explore the formation of GDM mothers’ breastfeeding behaviors based on the health belief model (HBM).Methods: A questionnaire survey was conducted on 324 GDM mothers who have given birth within 6 months from January 1 to February 6, 2022. According to HBM, GDM mothers’ knowledge, the perceived threat from GDM, the perceived value of breastfeeding, self-efficacy, social support and GDM mothers’ breastfeeding behavior were measured. Exclusive breastfeeding (EBF) was defined as an infant who received only breast milk in the past 24 h before the survey. Structural equation modeling (SEM) was applied to explore how GDM mothers form their breastfeeding behaviors based on HBM.Results: The prevalence of EBF among GDM mothers was 33.95%. GDM mothers had limited knowledge of GDM (average 63.14% correct answer to 7 questions), especially poor on the long-term effect of GDM (39.81%) and protective effect of breastfeeding (34.57%-45.99%). Although GDM mothers showed high perceived benefits (Mean: 3.35, SD: 0.46), high self-efficacy (Mean: 3.43, SD: 0.97) and high level of social support for breastfeeding (Mean: 3.74, SD: 0.74), the various barriers (Mean: 2.20, SD: 0.47) hindered their success in EBF. The SEM results showed that a higher level of social support and more self-efficacy of breastfeeding resulted in a higher likelihood of EBF, while the higher level of knowledge of GDM, perceived higher barriers and benefits of breastfeeding and higher susceptibility to GDM consequences led to less EBF.Conclusion: To promote EBF, physicians’ education, emphasizing the protective effect of breastfeeding and how to correct breastfeeding, is highly recommended. In addition, social support for GDM mothers is also important to reduce their barriers to breastfeeding and help enhance self-efficacy in breastfeeding.