MyGuide long COVID: An online self-management tool for people with long COVID
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
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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