The use of internet analytics by a Canadian provincial chiropractic regulator to monitor, evaluate and remediate misleading claims regarding specific health conditions, pregnancy, and COVID-19
Bibliographic record
Abstract
BACKGROUND: Internet analytics are increasingly being integrated into public health regulation. One specific application is to monitor compliance of website and social media activity with respect to jurisdictional regulations. These data may then identify breaches of compliance and inform disciplinary actions. Our study aimed to evaluate the novel use of internet analytics by a Canadian chiropractic regulator to determine their registrants compliance with three regulations related to specific health conditions, pregnancy conditions and most recently, claims of improved immunity during the COVID-19 crisis. METHODS: A customized internet search tool (Market Review Tool, MRT) was used by the College of Chiropractors of British Columbia (CCBC), Canada to audit registrants websites and social media activity. The audits extracted words whose use within specific contexts is not permitted under CCBC guidelines. The MRT was first used in October of 2018 to identify words related to specific health conditions. The MRT was again used in December 2019 for words related to pregnancy and most recently in March 2020 for words related to COVID-19. In these three MRT applications, potential cases of word misuse were evaluated by the regulator who then notified the practitioner to comply with existing regulations by a specific date. The MRT was then used on that date to determine compliance. Those found to be non-compliant were referred to the regulator's inquiry committee. We mapped this process and reported the outcomes with permission of the regulator. RESULTS: In September 2018, 250 inappropriate mentions of specific health conditions were detected from approximately 1250 registrants with 2 failing to comply. The second scan for pregnancy related terms of approximately1350 practitioners revealed 83 inappropriate mentions. Following notification, all 83 cases were compliant within the specified timeframe. Regarding COVID-19 related words, 97 inappropriate mentions of the word "immune" were detected from 1350 registrants with 7 cases of non-compliance. CONCLUSION: Internet analytics are an effective way for regulators to monitor internet activity to protect the public from misleading statements. The processes described were effective at bringing about rapid practitioner compliance. Given the increasing volume of internet activity by healthcare professionals, internet analytics are an important addition for health care regulators to protect the public they serve.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".