‘A name to the pain’: A mixed methods analysis of diagnostic delay and perceptions of diagnosis importance in Australians with endometriosis
Bibliographic record
Abstract
Diagnostic delay is a significant issue facing people with endometriosis; however, the Australian perspective and participant voice is missing for why delay occurs. The current study aimed to assess the length of diagnostic delay, whether this is changing over time, correlates of longer delay and the importance of diagnosis. This study utilised a mixed methods cross-sectional online survey of people with endometriosis (n = 506). Individuals with self-reported endometriosis were recruited via social media and websites of Australian endometriosis organisations completing an online, cross-sectional survey. Hierarchical multiple regression, ANOVA and template analysis were conducted. Participants reported an average diagnostic delay of 12.3 years (SD = 7.7), with delay appearing shorter in those who first saw a general practitioner (GP) for their symptoms since 2018 (mean 4.7 years, SD = 3.4). More recent endometriosis-related symptom onset, younger age at diagnosis, and accessing medical care through public healthcare were associated with shorter delays, whilst seeing a higher number of doctors prior to diagnosis and queer identity was associated with longer delays. Participants indicated that diagnostic delay most commonly occurs due to dismissal and disbelief by medical professionals and qualitative accounts revealed that receiving a diagnosis is important for many reasons. Diagnostic delay is perceived as a barrier to receiving timely, effective care for endometriosis. Increased societal and medical professional knowledge regarding symptoms indicative of endometriosis, and early treatment and clinical skills focused on pain validation and acknowledgement are recommended to improve timely diagnosis.
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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 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".