MyGuide long COVID: An online self-management tool for people with long COVID
Bibliographic record
Abstract
Long COVID is a relatively new condition for which patients are asked to employ self-management strategies to manage their symptoms. However, it can be challenging for individuals with long COVID to find reliable and actionable self-management resources. The objective of this project was to develop an online tool for individuals with long COVID that is patient-centered, accessible, and customizable to meet individual needs. MyGuide Long COVID ( www.longCOVIDguide.ca ) was developed in British Columbia (BC), Canada, by a team that included long COVID clinicians and patient partners. Site visitors answer questions about their symptoms, and MyGuide generates a curated set of self-management resources tailored to their needs. Since its launch in August 2023, Google Analytics has been used to monitor website activity. Within the first year, MyGuide had 52,578 total page views and 8570 new users. The most popular method to access MyGuide was by computer (56.3 % of users), and the most represented city was Vancouver, BC (23.5 % of users). The most popular topics were “Post Exertional Malaise” (1339 sessions) and “What is long COVID?” (1257 sessions). An online tool to support chronic disease self-management can be successfully co-developed with patient partners and engagement tracked using web analytics. • It can be challenging for people with long COVID to find actionable self-management resources. • Developed with input from clinicians and patients, MyGuide Long COVID curates a set of resources for site visitors. • Within the first year, MyGuide Long COVID had 52,578 page views and 8570 new users. • MyGuide Long COVID is an example of an online tool can be co-created with patient partners and engagement tracked using web analytics.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.013 |
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".