Negotiation of collective and individual candidacy for long Covid healthcare in the early phases of the Covid-19 pandemic: Validated, diverted and rejected candidacy
Bibliographic record
Abstract
This analysis of people's accounts of establishing their need and experiences of healthcare for long Covid (LC) symptoms draws on interview data from five countries (UK, US, Netherlands, Canada, Australia) during the first ∼18 months of the Covid-19 pandemic when LC was an emerging, sometimes contested, condition with scant scientific or lay knowledge to guide patients and professionals in their sense-making of often bewildering constellations of symptoms. We extend the construct of candidacy to explore positive and (more often) negative experiences that patients reported in their quest to understand their symptoms and seek appropriate care. Candidacy usually considers how individuals negotiate healthcare access. We argue a crucial step preceding individual claims to candidacy is recognition of their condition through generation of collective candidacy. “Vanguard patients” collectively identified, named and fought for recognition of long Covid in the context of limited scientific knowledge and no established treatment pathways. This process was technologically accelerated via social media use. Patients commonly experienced “rejected” candidacy(feeling disbelieved, discounted/uncounted and abandoned, and that their suffering was invisible to the medical gaze and society). Patients who felt their candidacy was “validated” had more positive experiences; they appreciated being believed and recognition of their changed lives/bodies and uncertain futures. More positive healthcare encounters were described as a process of “co-experting” through which patient and healthcare professional collaborated in a joint quest towards a pathway to recovery. The findings underpin the importance of believing and learning from patient experience, particularly vanguard patients with new and emerging illnesses.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".