Experiences of stigma and access to care among long COVID patients: a qualitative study in a multi-ethnic population in the Netherlands
Bibliographic record
Abstract
OBJECTIVE: This study explored the experience of stigma and access to healthcare by persons with long COVID from the majority Dutch and two ethnic minority populations (Turkish and Moroccan) living in the Netherlands. DESIGN: This was a cross-sectional qualitative study that employed inductive and deductive thematic approaches to data analysis using MAXQDA. SETTING AND PARTICIPANTS: Between October 2022 and January 2023, 23 semi-structured interviews were conducted with participants of Dutch, Moroccan and Turkish ethnic origins with long COVID living in the Netherlands. Participants were men and women aged 30 years and above. RESULTS: Guided by the concepts of stigma and candidacy, the findings are structured according to the broader themes of stigma and access to care. The findings show that people with long COVID suffer self and public stigma resulting from the debilitating illness and symptoms. Especially among Turkish and Moroccan ethnic minority participants, strong filial obligations and gendered expectations of responsibility and support within their communities further worsen self-stigma. This experience of stigma persisted within healthcare where lack of information and appropriate care pathways led to feelings of frustration and abandonment, especially for participants with pre-existing health conditions which further complicate candidacy. Under the access to healthcare theme, the findings show multiple challenges in accessing healthcare for long COVID due to several multifaceted factors related to the various stages of candidacy which impacted access to care. Particularly for Turkish and Moroccan ethnic minority participants, additional challenges resulting from limited access to information, pre-existing structural challenges and experience of stereotyping based on ethnicity or assumed migrant identity by health professionals further complicate access to health information and long COVID care. CONCLUSIONS: The findings call for urgent attention and research to identify and coordinate healthcare for long COVID. There is also a need for accessible, informative and tailored support systems to facilitate patients' access to information and care pathways for long COVID. Providing tailored information and support, addressing the various barriers that hinder optimal operating conditions in healthcare and leveraging on social networks is crucial for addressing stigma and facilitating candidacy for persons with long COVID towards improving access to care.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".