Individuals’ perceptions of Long Covid: a phenomenological approach to an online health community narratives
Bibliographic record
Abstract
BACKGROUND: In 2023, it was estimated that at least 65 million individuals had Long Covid (LC). Yet the literature reveals a lack of knowledge on how individuals perceive and experience LC symptoms. This study aims to explore how individuals with Long Covid describe their symptoms across physical, cognitive, emotional, social, and behavioural dimensions, and to analyse these experiences through the lens of the Symptom Management Theory (SMT) using a phenomenological and netnographic approach to spontaneous patient narratives. METHODS: A netnographic study was conducted on 63 selected participants in France from 19 April 2020 to 31 December 2022. Narratives were first analysed phenomenologically using TROPES software. Verbatims were then coded through content analysis with NVivo12Pro and organised according to the SMT dimensions of symptom experience. RESULTS: The study revealed that the testimonies of Long Covid patients are characterized by an argumentative, personal, and chronological discourse, highlighting the intensity of persistent symptoms and their significant impact on daily life. The most frequent symptoms identified include bodily pain, respiratory issues, chronic fatigue, as well as sensory and cognitive disturbances, along with significant emotional and social challenges. CONCLUSIONS: Our study demonstrates the profound and multidimensional impact of Long Covid on patients’ daily lives, highlighting the need for a holistic, integrated approach to its management, considering affective, cognitive, behavioural, physical, and social dimensions to improve patients’ quality of life. PRACTICAL IMPLICATIONS: Understanding the lived experiences of LC patients can guide healthcare services in providing more targeted, empathetic, and effective support.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".