Are virtual communities good for our health?
Bibliographic record
Abstract
The last decade of the 20th century will be remembered as the period when, perhaps for the first time in history, humans became overloaded by the hyperproduction and hyperdispersion of health information. From that point on, practically anyone with an internet account and access to a personal computer could adopt the role that a few years before had been reserved to professional editors, publishers, journalists, librarians, and academics.1 Ways of coping with this flood of information clearly need to change, and a paper in this week's BMJ suggests that they are changing (p 939)2—and in ways that challenge the established order within health systems.3 Health professionals are just as incapable of handling the increased flow of information as the public, despite their access to tools and techniques for evidence based decision making.4 Tools to assess the “quality” of information have been tried many times, unsuccessfully5; uncoordinated efforts to create “one stop shops,” seals of quality, and trusted filters for information1 have repeatedly failed: the Tower of Babel continues to rise. One hundred days into the outbreak of severe acute respiratory syndrome (SARS) in 2003, for instance, a search of the web using Google with the term SARS (which did not exist a few months before) yielded over five million hits. These included countless repetitive efforts to describe the disease, its risk factors, and mode of transmission. Ironically, these efforts made it even more difficult for the public to understand what was happening or what to do. It would be easy to think that there is no end in sight to the chaotic, open ended evolution of health information. However, the explosion of information and the ways to exchange it are inherently dynamic, discontinuous, and complex—all of which can lead to self organisation.6 This phenomenon has been observed in practically all fields and at all levels, from the subcellular to the astronomical.7. Self organisation is also the glue that maintains cohesion within virtual communities, providing their members with efficient ways to exchange useful information, provide or receive meaningful social support, and develop friendships.8,9 Esquivel et al this week report a study that shows how self organisation within an established virtual community on breast cancer enabled its members to identify and correct potentially harmful information.2 They examined 4600 postings sent during the first quarter of 2005 to the Breast Cancer Mailing List (www.bclist.org) and found that of the only 10 that they considered false or misleading seven were identified by multiple members of the list and corrected, on average, less than five hours after they had appeared. One can read between the lines of this article, however, to identify all sorts of benefits to members of virtual communities that go beyond the clinical information content and which the authors overlooked. We do not have a formal measure for how well the remaining 4597 messages helped the correspondents to adapt to the changing circumstances created by their experience of cancer—by sharing experiences and practical tips on everything from how to deal with the illness to how to deal with the health system. But the very existence of the list and its persistence for over a decade suggest that these benefits are real and sufficient to motivate the sending of more than a quarter of a million messages since its inception. The sheer number of virtual communities focused on health related matters is probably a surrogate marker of the gratification gained by their members. Yahoo Groups (http://groups.yahoo.com/) alone lists more than 7000 communities focused on health care, more than 6000 on alternative medicine, and more than 2000 on drugs and medications. A recent systematic review attempted to measure the value of virtual communities in the health sector through the traditional lens of quantitative research methods, missing the fact that such value transcends conventional measures of clinical outcome, resource use, or social support.10 We need to develop innovative strategies to help us understand the role that virtual communities play in helping people cope with complex health issues, such as those found during chronic diseases or while trying to change entrenched practices.11,12 In the 1970s Ivan Illich challenged us to view health as “the ability to adapt to changing environments, to growing up and to aging, to healing when damaged, to suffering, and to the peaceful expectation of death.” He also urged us to “demedicalise” life and to strive for minimal bureaucratic interference in mating; giving birth; regulating our daily rhythm; and coping with pain, sickness, and death.13 Virtual communities may turn out to be the agoras of the 21st century, electronic meeting places fuelled by the inexhaustible energy that exists at the edge of chaos, where the unpredictable interactions of millions of people might generate some of the answers to meet this challenge.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".