What advice is given to newly qualified doctors on Twitter? An analysis of #TipsForNewDocs tweets

Educational Advices for New-Graduate Nurse on Social Media in China: A Grounded Theory Study

Aims: This study aimed to excavate the entry advices and suggestions for new-graduate nurses. Background: Research based on social media analysis for advices to new-graduate nurses is rare. We conducted a detailed analysis and modularization of the relevant contents based on social media. Design: A grounded theory study based on social media content. Methods: The answers to questions such like "What's your advices and suggestions for new-graduate nurses?" on a famous social media platform in China were searched, collected, coded, and analyzed until September 2023. Results: Four core category modules including professional knowledge module, professional ability module, socialization and interpersonal communication module, and personal trait module were established. The theoretical framework of 21 main categories was also established. Conclusion: Social media contents provide valuable and experienced advices and suggestions for new-graduate nurses in an efficient and convenient way. The analysis of these contents is helpful for new-graduate nurse education and management.

An opportunity to be grateful for? Exploring discourses about international medical graduates from India and Pakistan to the UK between 1960 and 1980

INTRODUCTION

Following India and Pakistan gaining independence from British colonial rule, many doctors from these countries migrated to the UK and supported its fledgling National Health Service (NHS). Although this contribution is now widely celebrated, these doctors often faced hardship and hostility at the time and continue to face discrimination and racism in UK medical education. This study sought to examine discursive framings about Indian and Pakistani International Medical Graduates (IPIMGs) in the early period of their migration to the UK, between 1960 and 1980.

METHODS

We assembled a textual archive of publications relating to IPIMGs in the UK during this time period in The BMJ. We employed critical discourse analysis to examine knowledge and power relations in these texts, drawing on postcolonialism through the contrapuntal approach developed by Edward Said.

RESULTS

The dominant discourse in this archive was one of opportunity. This included the opportunity for training, which was not available to IPIMGs in an equitable way, the missed opportunity to frame IPIMGs as saviours of the NHS rather than 'cheap labour', and the opportunity these doctors were framed to be held by being in the 'superior' British system, for which they should be grateful. Notably, there was also an opportunity to oppose, as IPIMGs challenged notions of incompetence directed at them.

CONCLUSION

As IPIMGs in the UK continue to face discrimination, we shed light on how their cultural positioning has been historically founded and engrained in the imagination of the British medical profession by examining discursive trends to uncover historical tensions and contradictions.

Reluctant heroes: New doctors negotiating their identities dialogically on social media

BACKGROUND

Ensuring that students transition smoothly into the identity of a doctor is a perpetual challenge for medical curricula. Developing professional identity, according to cultural-historical activity theory, requires negotiation of dialectic tensions between individual agency and the structuring influence of institutions. We posed the research question: How do medical interns, other clinicians and institutions dialogically construct their interacting identities?

METHODS

Our qualitative methodology was rooted in dialogism, Bakhtin's cultural-historical theory that accounts for how language mediates learning and identity. Reasoning that the COVID pandemic would accentuate and expose pre-existing tensions, we monitored feeds into the Twitter microblogging platform during medical students' accelerated entry to practice; identified relevant posts from graduating students, other clinicians and institutional representatives; and kept an audit trail of chains of dialogue. Sullivan's dialogic methodology and Gee's heuristics guided a reflexive, linguistic analysis.

RESULTS

There was a gradient of power and affect. Institutional representatives used metaphors of heroism to celebrate 'their graduates', implicitly according a heroic identity to themselves as well. Interns, meanwhile, identified themselves as incapable, vulnerable and fearful because the institutions from which they had graduated had not taught them to practise. Senior doctors' posts were ambivalent: Some identified with institutions, maintaining hierarchical distance between themselves and interns; others, along with residents, acknowledged interns' distress, expressing empathy, support and encouragement, which constructed an identity of collegial solidarity.

CONCLUSIONS

The dialogue exposed hierarchical distance between institutions and the graduates they educated, which constructed mutually contradictory identities. Powerful institutions strengthened their identities by projecting positive affects onto interns who, by contrast, had fragile identities and sometimes strongly negative affects. We speculate that this polarisation may be contributing to the poor morale of doctors in training and propose that, to maintain the vitality of medical education, institutions should seek to reconcile their projected identities with the lived identities of graduates.

#TreatmentResistantDepression: A qualitative content analysis of Tweets about difficult-to-treat depression

INTRODUCTION

Treatment-resistant depression (TRD) is depression unresponsive to antidepressants and affects 55% of British primary care users with depression. Current evidence is from secondary care, but long referral times mean general practitioners (GPs) manage TRD. Studies show that people with depression use Twitter to form community and document symptoms. However, Twitter remains a largely unexplored space of documented patient experience. Twitter data could provide valuable insights into learning about primary care experiences of TRD. In this study, we explored Twitter comments and conversations about TRD and produced patient-driven recommendations.

METHODS

Tweets from UK-based users were collected manually and using a browser extension in June 2021. Conventional content analysis was used to provide an overview of the Tweets, followed by interpretation to understand why Twitter may be important to people with TRD.

RESULTS

A total of 415 Tweets were organised into five clusters: self-diagnosis, symptoms, support, small wins and condition experts. These Tweets were interpreted as showing Twitter as a community for people with TRD. People had a collective sense of illness identity and were united in their experiences of TRD. However, users in the community also highlighted the absence of effective GP care, leading users to position themselves as condition experts. Users shared advice from a place of lived experience with the community but also shared potentially harmful information, including recommendations about nonevidence-based medications.

CONCLUSIONS

Findings illuminate the benefits of the TRD Twitter community and also highlight that the perception of a lack of knowledge and support from GPs may lead community members to advise nonevidenced-based medications.

PATIENT AND PUBLIC CONTRIBUTION

This study was led by a person with lived experience of TRD and bipolar. Two public contributors with mental health conditions gave feedback on our study protocol and results.

Use of the Hashtag #DataSavesLives on Twitter: Exploratory and Thematic Analysis

Background

“Data Saves Lives” is a public engagement campaign that highlights the benefits of big data research and aims to establish public trust for this emerging research area.

Objective

This study explores how the hashtag #DataSavesLives is used on Twitter. We focused on the period when the UK government and its agencies adopted #DataSavesLives in an attempt to support their plans to set up a new database holding National Health Service (NHS) users’ medical data.

Methods

Public tweets published between April 19 and July 15, 2021, using the hashtag #DataSavesLives were saved using NCapture for NVivo 12. All tweets were coded twice. First, each tweet was assigned a positive, neutral, or negative attitude toward the campaign. Second, inductive thematic analysis was conducted. The results of the thematic analysis were mapped under 3 models of public engagement: deficit, dialogue, and participatory.

Results

Of 1026 unique tweets available for qualitative analysis, discussion around #DataSavesLives was largely positive (n=716, 69.8%) or neutral (n=276, 26.9%) toward the campaign with limited negative attitudes (n=34, 3.3%). Themes derived from the #DataSavesLives debate included ethical sharing, proactively engaging the public, coproducing knowledge with the public, harnessing potential, and gaining an understanding of big data research. The Twitter discourse was largely positive toward the campaign. The hashtag is predominantly used by similar-minded Twitter users to share information about big data projects and to spread positive messages about big data research when there are public controversies. The hashtag is generally used by organizations and people supportive of big data research. Tweet authors recognize that the public should be proactively engaged and involved in big data projects. The campaign remains UK centric. The results indicate that the communication around big data research is driven by the professional community and remains 1-way as members of the public rarely use the hashtag.

Conclusions

The results demonstrate the potential of social media but draws attention to hashtag usage being generally confined to “Twitter bubbles”: groups of similar-minded Twitter users.

Promoting Research, Awareness, and Discussion on AI in Medicine Using #MedTwitterAI: A Longitudinal Twitter Hashtag Analysis

Background

Artificial intelligence (AI) has the potential to reshape medical practice and the delivery of healthcare. Online discussions surrounding AI's utility in these domains are increasingly emerging, likely due to considerable interest from healthcare practitioners, medical technology developers, and other relevant stakeholders. However, many practitioners and medical students report limited understanding and familiarity with AI.

Objective

To promote research, events, and resources at the intersection of AI and medicine for the online medical community, we created a Twitter-based campaign using the hashtag #MedTwitterAI.

Methods

In the present study, we analyze the use of #MedTwitterAI by tracking tweets containing this hashtag posted from 26th March, 2019 to 26th March, 2021, using the Symplur Signals hashtag analytics tool. The full text of all #MedTwitterAI tweets was also extracted and subjected to a natural language processing analysis.

Results

Over this time period, we identified 7,441 tweets containing #MedTwitterAI, posted by 1,519 unique Twitter users which generated 59,455,569 impressions. The most common identifiable locations for users including this hashtag in tweets were the United States (378/1,519), the United Kingdom (80/1,519), Canada (65/1,519), India (46/1,519), Spain (29/1,519), France (24/1,519), Italy (16/1,519), Australia (16/1,519), Germany (16/1,519), and Brazil (15/1,519). Tweets were frequently enhanced with links (80.2%), mentions of other accounts (93.9%), and photos (56.6%). The five most abundant single words were AI (artificial intelligence), patients, medicine, data, and learning. Sentiment analysis revealed an overall majority of positive single word sentiments (e.g., intelligence, improve) with 230 positive and 172 negative sentiments with a total of 658 and 342 mentions of all positive and negative sentiments, respectively. Most frequently mentioned negative sentiments were cancer, risk, and bias. Most common bigrams identified by Markov chain depiction were related to analytical methods (e.g., label-free detection) and medical conditions/biological processes (e.g., rare circulating tumor cells).

Conclusion

These results demonstrate the generated considerable interest of using #MedTwitterAI for promoting relevant content and engaging a broad and geographically diverse audience. The use of hashtags in Twitter-based campaigns can be an effective tool to raise awareness of interdisciplinary fields and enable knowledge-sharing on a global scale.

Sharing is caring: an analysis of #FOAMed Twitter posts during the COVID-19 pandemic

PURPOSE

Free Open Access Medical Education (FOAMed) is a worldwide social media movement designed to accelerate and democratise the sharing of medical knowledge. This study sought to investigate the content shared through FOAMed during the emerging COVID-19 pandemic.

STUDY DESIGN

Tweets containing the #FOAMed hashtag posted during a 24-hour period in April 2020 were studied. Included tweets were analysed using the Wiig knowledge management cycle framework (building knowledge, holding knowledge, pooling knowledge and using knowledge).

RESULTS

1379 tweets contained the #FOAMed hashtag, of which 265 met the inclusion criteria and were included in the analysis. Included tweets were posted from 208 distinct users, originated from each world continent and were in five different languages. Three overarching themes were identified: (1) signposting and appraising evidence and guidelines; (2) sharing specialist and technical advice; and (3) personal and social engagement. Among 12 subthemes within these groupings, 11 aligned to one of the four dimensions of the Wiig knowledge management cycle framework, and the other focused on building and managing social networks. Almost 40% of tweets related directly to COVID-19.

CONCLUSION

#FOAMed tweets during the COVID-19 pandemic included a broad range of resources, advice and support. Despite the geographical, language and disciplinary variation of contributing users and the lack of organisational structure uniting them, this social media medical community has been able to construct, share and use emerging technical knowledge through a time of extraordinary challenge and uncertainty for the global medical community.

Analysis of Cyberincivility in Posts by Health Professions Students: Descriptive Twitter Data Mining Study

BACKGROUND

Health professions students use social media to communicate with other students and health professionals, discuss career plans or coursework, and share the results of research projects or new information. These platforms allow students to share thoughts and perceptions that are not disclosed in formal education settings. Twitter provides an excellent window through which health professions educators can observe students' sociocultural and learning needs. However, despite its merits, cyberincivility on Twitter among health professions students has been reported. Cyber means using electronic technologies, and incivility is a general term for bad manners. As such, cyberincivility refers to any act of disrespectful, insensitive, or disruptive behavior in an electronic environment.

OBJECTIVE

This study aims to describe the characteristics and instances of cyberincivility posted on Twitter by self-identified health professions students. A further objective of the study is to analyze the prevalence of tweets perceived as inappropriate or potentially objectionable while describing patterns and differences in the instances of cyberincivility posted by those users.

METHODS

We used a cross-sectional descriptive Twitter data mining method to collect quantitative and qualitative data from August 2019 to February 2020. The sample was taken from users who self-identified as health professions students (eg, medicine, nursing, dental, pharmacy, physician assistant, and physical therapy) in their user description. Data management and analysis were performed with a combination of SAS 9.4 for descriptive and inferential statistics, including logistic regression, and NVivo 12 for descriptive patterns of textual data.

RESULTS

We analyzed 20 of the most recent tweets for each account (N=12,820). A total of 639 user accounts were analyzed for quantitative analysis, including 280 (43.8%) medicine students and 329 (51.5%) nursing students in 22 countries: the United States (287/639, 44.9%), the United Kingdom (197/639, 30.8%), unknown countries (104/639, 16.3%), and 19 other countries (51/639, 8.0%). Of the 639 accounts, 193 (30.2%) were coded as having instances of cyberincivility. Of these, 61.7% (119/193), 32.6% (63/193), and 5.7% (11/193) belonged to students in nursing, medicine, and other disciplines, respectively. Among 502 instances of cyberincivility identified from 641 qualitative analysis samples, the largest categories were profanity and product promotion. Several aggressive or biased comments toward other users, politicians, or certain groups of people were also found.

CONCLUSIONS

Cyberincivility is a multifaceted phenomenon that must be considered in its complexity if health professions students are to embrace a culture of mutual respect and collaboration. Students' perceptions and reports of their Twitter experiences offer insights into behavior on the web and the evolving role of cyberspace, and potentially problematic posts provide opportunities for teaching digital professionalism. Our study indicates that there is a continued need to provide students with guidance and training regarding the importance of maintaining a professional persona on the web.

Twitter as a Mental Health Support System for Students and Professionals in the Medical Field

Twitter is a rapidly growing social media site that has greatly integrated itself in the lives of students and professionals in the medical field. While Twitter has been found to be very helpful in facilitating education, there is also great potential for its usage as a social support system. Social support has become more essential as society grapples with declining mental health, particularly in the medical sector. In our previous paper, we saw that Twitter provides a promising tool to learn more about the online conversation about dementia and, in particular, the supportive network that can be created. Inspired by this, we decided to investigate the potential of using Twitter as a support system for students and professionals in the medical field. In this paper, we explore the current state of mental health in the medical field and suggest practical implementation methods for using Twitter.

Twelve tips for developing and implementing a medical education Twitter chat

Live discussions on the social media site Twitter or Twitter chats are gaining popularity as powerful tools for engaging a broad audience in an interactive discussion. Medical education, in particular, is experiencing an increase in the use of this modality to support informal learning, as a means to encourage collaboration and share best practices, and as a platform for large-scale mentorship. Despite this growth in popularity, there are limited data to guide medical educators on the fundamentals of organizing a Twitter chat. In this Twelve Tips article, we discuss strategies relevant to potential Twitter chat organizers. We have arranged the tips chronologically, beginning with a discussion of initial considerations when planning and formulating a chat topic and publicizing the chat to potentially interested people and groups, followed by practical considerations while hosting the chat, and finally strategies for evaluating and extending a Twitter chat's impact.

Qualitative research: An overview of emerging approaches for data collection

OBJECTIVE

The aim of this study was to introduce new approaches to conduct qualitative research that may provide valuable insight into issues related to education, training and patient care in psychiatry.

CONCLUSIONS

A variety of data-collection tools is available for researchers and practitioners in psychiatry. These can be used independently or in conjunction with other quantitative and qualitative methods.