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
Health care professionals often cringe when they hear the name “Dr. Oz.” This physician turned television personality has become a household name and a trusted source of health care information for many people across North America. Indeed, 3.4 million viewers1 tune into his show every day to hear about health conditions, treatments, tests and many other topics relevant to health care. While Dr. Oz has received considerable interest in recent years, pop culture media’s influence on health behaviour is not new. Oprah Winfrey has given us advice on dieting, Tom Cruise has warned us against antidepressant use in postpartum depression, while Jenny McCarthy advises against vaccinating our kids because of the risk for autism. Alternative sources of health information are so prevalent that it would be naive for health care professionals to assume that patients are completely loyal to the information that they provide. Currently, there is little objective information to quantify the effect of pop culture media on health behaviour. However, the signals are clear: after being featured on “The Dr. Oz Show,”2 neti pot sales increased by 12,000%, with Internet searches on the topic increasing 42,000%. In addition, a simple analysis using Google Trends shows a higher use of search terms for raspberry ketones3 and green coffee bean extract4 following episodes of “The Dr. Oz Show”5,6 in which these products were discussed (Figure 1). In each case, Internet searches with these terms were virtually nonexistent until the dates on which these episodes were aired. It is plausible that pharmacists and drug information centres also received requests for information during these same times. Figure 1 The episode air and re-air dates (as indicated by the arrows) for raspberry ketones5 and green coffee bean extract6 Few would argue that media sources often increase awareness about alternative health products, while simultaneously raising concerns about current health care practices. However, health care practitioners have no way of quantifying the prevalence or consequences of these trends. In our view, the frequent use of search terms identified with Google Trends could be a marker of the level of interest in a particular health product. Further, high levels of interest may also be associated with higher product sales. Personal communication with a single natural product store in Lloydminster, Alberta, supports this hypothesis. Raspberry ketones were introduced to the store product line 5 weeks prior to the corresponding Dr. Oz episode, and sales appeared to increase sharply thereafter (Figure 2). Figure 2 Sales data on raspberry ketones from a natural product store The influence of media is not just restricted to sales of alternative products. An episode from “The Dr. Oz Show” focusing on the necessity of an “LDL particle size test”7 was followed by a spike in searches on Google Trends (Figure 3). This observation raises a few questions. First, does the increased level of interest in a blood test translate into higher usage in clinical settings? Second, if health services such as blood tests are vulnerable to pop media influences, who is paying the costs of these tests? Patients may be paying out of pocket to have these tests performed, but it is also possible that health insurance organizations or governments are ending up with the bill. Currently, we can find no information on the extent to which health insurance providers or governments are affected by social media. Figure 3 Internet search interest for the “LDL particle size test.” The peak points directly correspond to the episode air dates8 Patient nonadherence could be another important consequence of media influences. Take for example a recent episode of “The Dr. Oz Show” entitled, “The Doctors Who Say Everything You Know About Cholesterol Is Wrong.”7 Not surprisingly, issues around statin use for high cholesterol were discussed, and viewers (especially those taking statins) could have easily concluded that their prescriptions were inappropriate. Ultimately, negative health outcomes could occur if media sources are influencing patients to take unproven products, quit effective medications or undergo unsanctioned procedures. There is already evidence for the dire consequences of medication nonadherence, but our understanding of its causes remains limited. Perhaps a greater understanding of how the media influence health behaviour might help us to devise more effective strategies to reduce these problems. Moreover, health care practitioners urgently require training on how to help patients navigate and interpret the ever-growing number of messages from media sources. At minimum, we need to be able to effectively reduce bias in our interactions with patients.9 For now, it must be recognized that sources of media are conveying health-related messages that are likely influencing patients.10 Health care professionals must not dismiss information obtained by patients regardless of the credibility of the source. Instead, it is crucial for health care professionals to recognize that their patients simply want to get better. Open dialogue with an empathetic response to their desire for good news about safe remedies is the best approach to establishing trust and facilitating safe medication use. Remember that patients probably cringe every time they hear health care practitioners criticize alternative therapies. ■
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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