Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In early 2023, we published a call for articles for a special issue of Nursing Research on innovative research designed to counteract health misinformation. We were looking forward to receiving many submissions on this important topic. Unfortunately, that did not happen. We had a few queries about the call, but those queries were not followed by articles. We had only a handful of submissions related to the call; none was accepted for publication. We did publish a few articles in 2023 that partly addressed health misinformation (i.e., Jones et al., 2023; Zha et al., 2023), but we are puzzled by the limited response to the call. Research about ways to provide accurate health information and about how health information is understood and used within different groups of people is clearly within the scope of nursing science. We know that researchers generally have found that healthcare providers, including nurses, do little to correct health misinformation, especially on social media (Bautista et al., 2021), although provider intent to correct misinformation is relatively high. Whether this lack of action is related to not knowing how to correct misinformation, an absence of organizational support to correct information posted on social media, or indifference to the problem is not known. This lack of action by healthcare providers to correct misinformation is one aspect of the misinformation phenomenon that requires researcher attention. More broadly, research is needed about how to distribute accurate health information while also correcting health misinformation. Health misinformation thrives in the absence of easily accessible, credible information; when there is limited or contradictory information; or when the information is emotionally conveyed (Sylvia Chou et al., 2020). Moreover, acceptance of misinformation may result from an inability to distinguish between accurate and inaccurate data, an unwillingness to make distinctions between accurate or inaccurate information, or both. There may also be uncertainty about a health situation or an information void, which increases the potential for rapid spread of misinformation (i.e., COVID and other recent epidemics). Uncertainty is generally a bad thing where health is concerned as persons may accept dangerous or erroneous information as true to fill knowledge gaps. At the same time, individuals may also fail to perceive a problem with the information they are using to guide health decisions, believing instead they are making good health decisions even when they are not (i.e., vaccine avoidance in highly educated parents). In addition, misinformation can spread more rapidly when there is societal division and in situations of distrust. This may result in persons accepting misinformation as true even if they have access to scientifically accurate information. Recipients may also accept misinformation as true because of historical or contextual legacies (Southwell et al., 2023). In other words, inaccurate and false health-related messages do not alone constitute misinformation (Krishna & Thompson, 2021); the public use of misinformation is integral to the problem. Nursing science has a clear mandate within the phenomenon of misinformation. In particular, nursing scientists have a long-standing interest in improving health literacy, defined as the ability to obtain, process, and understand basic health information and services to make appropriate health decisions (Parker et al., 2003). The spread of health misinformation would be severely limited if there was increased health literacy among all populations and especially among those populations for whom there are known health disparities. Counteracting health misinformation could result in a reduction of health disparities and thus, perhaps, an improvement in health equity, especially among populations vulnerable to misunderstanding. Health misinformation is a threat to public health and human well-being (Jackson et al., 2021). Research is needed to better understand how people are exposed to and affected by misinformation and how misinformation varies across populations. Research is also needed about effective strategies to prevent and address health misinformation and to improve persons' ability to make informed decisions about health and healthcare. It is particularly important to rigorously test the effectiveness of strategies for distributing accurate health information as well as the effectiveness of strategies to correct misinformation. Research about these topics and interventions to improve health literacy, including digital health literacy, are needed. These areas of research are within the scope and mandate of nursing science. Here at Nursing Research, we are open to receiving articles reporting this work so that we may learn and go forward together in our efforts to improve health outcomes for all.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it