“None of us are lying”: an interpretive description of the search for legitimacy and the journey to access quality health services by individuals living with Long COVID
Bibliographic record
Abstract
BACKGROUND: Understanding of Long COVID has advanced through patient-led initiatives. However, research about barriers to accessing Long COVID services is limited. This study aimed to better understand the need for, access to, and quality of, Long COVID services. We explored health needs and experiences of services, including ability of services to address needs. METHODS: Our study was informed by the Levesque et al.'s (2013) "conceptual framework of access to health care." We used Interpretive Description, a qualitative approach partly aimed at informing clinical decisions. We recruited participants across five settings. Participants engaged in one-time, semi-structured, virtual interviews. Interviews were transcribed verbatim. We used reflexive thematic analysis. Best practice to ensure methodological rigour was employed. RESULTS: Three key themes were generated from 56 interviews. The first theme illustrated the rollercoaster-like nature of participants' Long COVID symptoms and the resulting impact on function and health. The second theme highlighted participants' attempts to access Long COVID services. Guidance received from healthcare professionals and self-advocacy impacted initial access. When navigating Long COVID services within the broader system, participants encountered barriers to access around stigma; appointment logistics; testing and 'normal' results; and financial precarity and affordability of services. The third theme illuminated common factors participants liked and disliked about Long COVID services. We framed each sub-theme as the key lesson (stemming from all likes and dislikes) that, if acted upon, the health system can use to improve the quality of Long COVID services. This provides tangible ways to improve the system based directly on what we heard from participants. CONCLUSION: With Long COVID services continuously evolving, our findings can inform decision makers within the health system to better understand the lived experiences of Long COVID and tailor services and policies appropriately.
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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 | 0.026 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.026 | 0.055 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".