‘A gift and a curse’: the benefits and limitations of self-tracking Long COVID
Bibliographic record
Abstract
People living with Long COVID are dealing with significant challenges related to limited understanding of this novel condition, social stigma, and lack of support from medical professionals and others in their lives. This article discusses findings from a qualitative study about how people with Long COVID have spontaneously engaged in self-tracking for the purposes of understanding and managing their illness. It draws on 30 semi-structured interviews with study participants in the USA, UK, Australia, Germany, Denmark and Canada. The study’s findings reveal that the personal health data generated by people with Long COVID through practices of self-tracking create new forms of knowledge about a novel post-viral condition and to some extent challenge the power differentials and fraught sociopolitical climate of the pandemic. The benefits provided by self-tracking data reflect the often psychologised and understudied position of post-viral conditions such as Long COVID. All participants described self-tracking as a valuable tool to gain insight into symptoms and evaluate interventions. It provided them with a sense of empowerment, control, encouragement, and very importantly, validation. However, for some participants, self-tracking their Long COVID symptoms was also sometimes experienced as overwhelming, anxiety-inducing, and frustrating. The study findings are interpreted with references to the broader contexts of novel chronic illness, medical power, lay expertise, COVID politics and digitised information and care work.
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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 | 0.057 | 0.111 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".