INVESTIGATING CANADIANS’ INFORMATION NEEDS RELATED TO LUPUS: A GOOGLE TRENDS ANALYSIS OF ONLINE SEARCH QUERY DATA
Bibliographic record
Abstract
PV096 / #835 Poster Topic: AS11 - Epidemiology and Public Health Background/Purpose SLE impacts 1 in 2000 Canadians, with indirect impacts on caregivers, households, family members, and healthcare professionals. SLE is idiosyncratic, with largely invisible symptoms that vary significantly from person to person, and disproportionately impacts groups considered vulnerable such as women, racialized, and low-income populations. These inequities are further underscored by a lack of public education and awareness about SLE, leading to delays in diagnosis and critical care and support. Indeed, lack of knowledge surrounding SLE has been identified as a main challenge for patients, particularly those seeking a diagnosis or recently diagnosed. Facing this challenge, many turn to online sources for information, where they risk encountering misleading or even endangering mis- or disinformation. The purpose of this research is to investigate public awareness of SLE and how this varies spatially across Canada using health geographical approaches to examine Google Trends (GT) data. Methods This research employs a health geographical approach to exploring spatial and temporal trends in information-seeking behaviors and associated knowledge gaps related to SLE in Canada. Using GT, relative search volumes (RSV), associated topics and queries were collected from 2004-present, using key words for “lupus.” The top 25 search terms were collected from each province and territory, and these search terms were analyzed thematically. The research process leveraged an integrated knowledge translation approach (iKT), in which a patient partner living with SLE was a core member of the research team. Results Search volumes for the search term “lupus” in Canada hit an all-time peak in October 2015 (RSV=1.0). This peak occurred in all provinces simultaneously, correlating with celebrity Selena Gomez’s diagnosis with SLE. Additional peaks were observed across Canada in July 2009 (RSV=0.58), September 2016 (RSV=0.69), and September 2017 (RSV=0.78), all of which were correlated with milestones in the development and eventual approval of belimumab for SLE. Similarly, a peak in August 2016 (RSV=0.66) was associated with positive Phase II trials for voclosporin. A national peak in June 2010 (RSV=0.60) was associated with the 9 th International Congress on SLE held in Vancouver. There was a marked trough across all provinces in November and December of 2020 (RSV=0.28), perhaps reflecting that SLE-related concerns were overshadowed by the ongoing COVID-19 pandemic. Overall interest was highest in Newfoundland (RSV=1.0), New Brunswick (RSV=0.83) and Manitoba (RSV=0.80), though the top related topics and queries varied spatially among provinces. The most frequently searched terms typically fell within the following themes: causes of lupus, diagnosis, symptoms, medication, and treatment. Some search terms were spatially unique, only appearing in 1 province, including search terms in French (“lupus maladie”, Quebec), and Indonesian (“penyakit lupus”, Newfoundland) languages. Conclusions An understanding of the information needs of the general public related to SLE, and how they vary spatially, is critical for designing and implementing targeted and effective patient education interventions. To this end, these research results will be shared and triangulated with the knowledge needs of advocacy organizations, and the realities of lived SLE experience, through a deliberative dialogue with key stakeholders from across Canada. This will set a foundation for the design and implementation of relevant interventions to effectively reduce SLE-related health disparities and improve SLE-related quality of life nationwide.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.028 | 0.077 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".