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Given the rapid development of social media, effective extraction and analysis of the contents of social media for health care have attracted widespread attention from health care providers. As far as we know, most of the reviews focus on the application of social media, and there is a lack of reviews that integrate the methods for analyzing social media information for health care. This scoping review aims to answer the following 4 questions: (1) What types of research have been used to investigate social media for health care, (2) what methods have been used to analyze the existing health information on social media, (3) what indicators should be applied to collect and evaluate the characteristics of methods for analyzing the contents of social media for health care, and (4) what are the current problems and development directions of methods used to analyze the contents of social media for health care? A scoping review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was conducted. We searched PubMed, the Web of Science, EMBASE, the Cumulative Index to Nursing and Allied Health Literature, and the Cochrane Library for the period from 2010 to May 2023 for primary studies focusing on social media and health care. Two independent reviewers screened eligible studies against inclusion criteria. A narrative synthesis of the included studies was conducted. Of 16,161 identified citations, 134 (0.8%) studies were included in this review. These included 67 (50.0%) qualitative designs, 43 (32.1%) quantitative designs, and 24 (17.9%) mixed methods designs. The applied research methods were classified based on the following aspects: (1) manual analysis methods (content analysis methodology, grounded theory, ethnography, classification analysis, thematic analysis, and scoring tables) and computer-aided analysis methods (latent Dirichlet allocation, support vector machine, probabilistic clustering, image analysis, topic modeling, sentiment analysis, and other natural language processing technologies), (2) categories of research contents, and (3) health care areas (health practice, health services, and health education). Based on an extensive literature review, we investigated the methods for analyzing the contents of social media for health care to determine the main applications, differences, trends, and existing problems. We also discussed the implications for the future. Traditional content analysis is still the mainstream method for analyzing social media content, and future research may be combined with big data research. With the progress of computers, mobile phones, smartwatches, and other smart devices, social media information sources will become more diversified. Future research can combine new sources, such as pictures, videos, and physiological signals, with online social networking to adapt to the development trend of the internet. More medical information talents need to be trained in the future to better solve the problem of network information analysis. Overall, this scoping review can be useful for a large audience that includes researchers entering the field.

The innovative method of sentiment analysis based on an emotional lexicon shows prominent advantages in capturing emotional information, such as individual attitudes, experiences, and needs, which provides a new perspective and method for emotion recognition and management for patients with breast cancer (BC). However, at present, sentiment analysis in the field of BC is limited, and there is no emotional lexicon for this field. Therefore, it is necessary to construct an emotional lexicon that conforms to the characteristics of patients with BC so as to provide a new tool for accurate identification and analysis of the patients' emotions and a new method for their personalized emotion management. This study aimed to construct an emotional lexicon of patients with BC. Emotional words were obtained by merging the words in 2 general sentiment lexicons, the Chinese Linguistic Inquiry and Word Count (C-LIWC) and HowNet, and the words in text corpora acquired from patients with BC via Weibo, semistructured interviews, and expressive writing. The lexicon was constructed using manual annotation and classification under the guidance of Russell's valence-arousal space. Ekman's basic emotional categories, Lazarus' cognitive appraisal theory of emotion, and a qualitative text analysis based on the text corpora of patients with BC were combined to determine the fine-grained emotional categories of the lexicon we constructed. Precision, recall, and the F1-score were used to evaluate the lexicon's performance. The text corpora collected from patients in different stages of BC included 150 written materials, 17 interviews, and 6689 original posts and comments from Weibo, with a total of 1,923,593 Chinese characters. The emotional lexicon of patients with BC contained 9357 words and covered 8 fine-grained emotional categories: joy, anger, sadness, fear, disgust, surprise, somatic symptoms, and BC terminology. Experimental results showed that precision, recall, and the F1-score of positive emotional words were 98.42%, 99.73%, and 99.07%, respectively, and those of negative emotional words were 99.73%, 98.38%, and 99.05%, respectively, which all significantly outperformed the C-LIWC and HowNet. The emotional lexicon with fine-grained emotional categories conforms to the characteristics of patients with BC. Its performance related to identifying and classifying domain-specific emotional words in BC is better compared to the C-LIWC and HowNet. This lexicon not only provides a new tool for sentiment analysis in the field of BC but also provides a new perspective for recognizing the specific emotional state and needs of patients with BC and formulating tailored emotional management plans.

Background Social networks have become a vital space for breast cancer (BC) patients to share deeply personal emotions they might avoid expressing in real life. However, the unstructured and vast nature of these textual expressions poses challenges for manual analysis. To address this, our research team employed transfer learning methods to efficiently process and analyze large-scale text data for depression detection. Objective This study seeks to address the emotional struggles faced by women with BC, who often grapple with depression but lack accessible mental health support. This study aims to develop a transfer learning-based model to enable timely, non-invasive identification of depression through patients’ self-expressed texts, thereby offering a pathway to early intervention. Methods A mixed-methods framework integrated qualitative content analysis with deep learning. We recruited 300 BC patients (inpatients and online users). Depression status was assessed via the Self-rating Depression Scale (SDS), followed by collection and preprocessing of their self-expressed texts. Texts were manually annotated for depression scores/status, and formed a corpus. Content analysis was used to explore linguistic features. A BERT-based model pre-trained on a Weibo depression corpus was fine-tuned using clinical texts. Performance was evaluated via five-fold cross-validation, adversarial testing (word replacement, misspelling, deletion), and ablation studies. Model performance was evaluated using accuracy, precision, recall, and F 1 -score. The model was validated by robustness analysis and ablation studies. Results Participants were grouped into depressive (n = 88) and non-depressive (n = 212) cohorts, while financial burden ( P  = 0.025) and advanced cancer stage ( P  = 0.038) correlated with depression. Content analysis revealed significant differences in negative life attitudes ( P  < 0.05). The transfer learning model achieved 86.67% accuracy (F 1 -score = 0.79). The model demonstrated robustness to semantic noise but required spelling correction for clinical deployment. Conclusion This study established a culturally adapted detection framework. By combining social media pre-training and clinical fine-tuning, the model enables scalable, non-invasive depression screening, bridging cultural barriers to emotional disclosure. Future work should expand demographic diversity and integrate multimodal data for enhanced clinical utility.

Background International attention is being paid to the issue of making evidence sustainable after implementation. Developing an identification model is essential to promote and monitor the sustainability of evidence implementation. However, this model is not available in Chinese. This study aims to translate the National Health Service Sustainability Model into Chinese and to verify whether the model is adapted to the Chinese healthcare environment. Methods This study follows the translation and validation guidelines developed by Sousa and Rojjanasrirat. The translations include forward and backward translations and their comparison. Expert reviews were used to validate the content validity of the Chinese version of the National Health Service sustainability model. Cognitive interviews were used to assess the validity of the language in the Chinese setting. Results The translation was conducted by a bilingual research team and took 12 months. Expert reviews were undertaken with eight experts, and cognitive interviews with six participants. The content validity of the model is excellent, but at least 20% of the experts still felt that items one, three, five and nine needed refinements. In the cognitive interviews, most items, instructions and response options were well understood by the participants responsible for the evidence-based practice project. However, some language issues were still identified in items one, three, four, five, seven, nine, and ten. Participants reported that the sustainability results of the model assessment were consistent with their previous judgments of the items. Based on the expert review and interview results, items one, three, four, five, seven, nine and ten require further refinement. In summary, seven of the ten items have been amended. Conclusions This study provides insight into how the National Health Service sustainability model can be used in the Chinese healthcare setting and paves the way for future large-scale psychometric testing.

Background Chemotherapy, whilst treating tumours, can also lead to numerous adverse reactions such as nausea and vomiting, fatigue and kidney toxicity, threatening the physical and mental health of patients. Simultaneously, misuse of chemotherapeutic drugs can seriously endanger patients' lives. Therefore, to maintain the safety of chemotherapy for cancer patients and to reduce the incidence of adverse reactions to chemotherapy, many guidelines state that a comprehensive assessment of the cancer patient should be conducted and documented before chemotherapy. This recommended procedure, however, has yet to be extensively embraced in Chinese hospitals. As such, this study aimed to standardise the content of pre-chemotherapy assessment for cancer patients in hospitals and to improve nurses' adherence to pre-chemotherapy assessment of cancer patients by conducting a national multi-site evidence implementation in China, hence protecting the safety of cancer patients undergoing chemotherapy and reducing the incidence of adverse reactions to chemotherapy in patients. Methods The national multi-site evidence implementation project was launched by a JBI Centre of Excellence in China and conducted using the JBI approach to evidence implementation. A pre- and post-audit approach was used to evaluate the effectiveness of the project. This project had seven phases: training, planning, baseline audit, evidence implementation, two rounds of follow-up audits (3 and 9 months after evidence implementation, respectively) and sustainability assessment. A live online broadcast allowed all participating hospitals to come together to provide a summary and feedback on the implementation of the project. Results Seventy-four hospitals from 32 cities in China participated in the project, four withdrew during the project's implementation, and 70 hospitals completed the project. The pre-and post-audit showed a significant improvement in the compliance rate of nurses performing pre-chemotherapy assessments for cancer patients. Patient satisfaction and chemotherapy safety were also improved through the project's implementation, and the participating nurses' enthusiasm and belief in implementing evidence into practice was increased. Conclusion The study demonstrated the feasibility of academic centres working with hospitals to promote the dissemination of evidence in clinical practice to accelerate knowledge translation. Further research is needed on the effectiveness of cross-regional and cross-organisational collaborations to facilitate evidence dissemination.

Psychological distress (PD) is one of the most prevalent psychological challenges among young patients with thyroid cancer. Recognizing the symptoms of psychological distress among young cancers at different stages is essential for improving their quality of life. This study aims to identify distinct profiles of psychological distress in young thyroid cancer patients post-surgery and assess differences in self-management efficacy across these profiles. This cross-sectional study was carried out in one general hospital in Chongqing, China. Participants completed the data collection on sociodemographic information, the specific Cancer Distress Scales for Adolescents and Young Adults (CDS-AYA), and the Strategies Used by People to Promote Health (SUPPH). Latent profile analysis was utilized to classify psychological distress into distinct subgroups, and analysis of covariance (ANCOVA) was employed to examine differences in self-management efficacy across these subgroups. A total of 213 valid questionnaires were collected. Ultimately, three distinct profiles of psychological distress were identified: \"low PD group\" (67.1%), \"moderate PD group\" (25.8%), and \"high PD group\" (7.1%). Statistically significant differences were observed among these groups with respect to monthly economic income, underlying diseases, treatment modalities, tumor node metastasis (TNM) staging, and cervical lymph node dissection (F = 36.308, < 0.001). Additionally, there were statistically significant variations in self-management efficacy scores across the three subgroups. Healthcare professionals ought to implement targeted interventions to tackle the heterogeneity of psychological distress, thereby assisting young with thyroid cancer in lowering their level of psychological distress and enhancing their ability to self-manage their disease.

Purpose The confidence in a study will be reduced due to the incorrect representation of statistical results. However, it is unknown to what extent p values are incorrectly represented in published nursing journals. The study aims to evaluate the articles in 30 nursing journals in terms of the error in reporting of p values (p = .000). Design and Methods This was a bibliometric analysis. All papers published in 10 leading nursing journals (between 2015 and 2019), the 10 bottom nursing journals (2019), and 10 selected key nursing journals (2019) indexed in the Science Citation Index Journal Citation Reports were reviewed to detect errors in reporting of p values (p = .000). Results A total of 3,788 papers were reviewed. Notably, it was found that 93.3% (28/30) of the nursing journals contained incorrect representation of p values (p = .000). The reporting rate of these journals ranges from 0% to 57.1%, with an overall rate of 12.8% (486/3,788). In addition, the rate of incorrect representation of p values (p = .000) showed no statistically significant difference between different publication years (Χ2 = 4.976, p = .290). However, the rate of reporting was different between study types, journals, and regions (p = .007, p = .020, and p < .001, respectively). Conclusions The incorrect representation of p values is common in nursing journals. Clinical Relevance We recommend that both publishers and researchers be responsible for preventing statistical errors in manuscripts. Furthermore, various kinds of statistical training methods should be adopted to ensure that nurses and journal reviewers have enough statistical literacy.

To explore whether emotional expressivity and the patterns of language use could predict benefits from expressive writing (EW) of breast cancer (BC) patients in a culture that strongly discourages emotional disclosure. Data were obtained from a recent trial in which we compared the health outcomes between a prolonged EW group (12 sessions) and a standard EW group (four sessions) ( = 56 per group) of BC patients receiving chemotherapy. The Chinese texts were tokenized using the THU Lexical Analyser for Chinese. Then, LIWC2015 was used to quantify positive and negative affect word use. Our first hypothesis that BC patients with higher levels of emotional expressivity tended to use higher levels of positive and negative affect words in texts was not supported ( = 0.067, = 0.549 and = 0.065, = 0.559, respectively). The level of emotional expressivity has a significant effect on the quality of life (QOL), and those who used more positive or fewer negative affective words in texts had a better QOL (all < 0.05). However, no significant difference was identified in physical and psychological well-being (all 0.05). Furthermore, the patterns of affective word use during EW did not mediate the effects of emotional expressivity on health outcomes (all > 0.05). Our findings suggest that the level of emotional expressivity and the pattern of affective word use could be factors that may moderate the effects of EW on QOL, which may help clinicians identify the individuals most likely to benefit from such writing exercises in China.