Self-management strategies to consider to combat endometriosis symptoms during the COVID-19 pandemic
Bibliographic record
Abstract
The care of patients with endometriosis has been complicated by the coronavirus disease 2019 (COVID-19) pandemic. Medical and allied healthcare appointments and surgeries are being temporarily postponed. Mandatory self-isolation has created new obstacles for individuals with endometriosis seeking pain relief and improvement in their quality of life. Anxieties may be heightened by concerns over whether endometriosis may be an underlying condition that could predispose to severe COVID-19 infection and what constitutes an appropriate indication for presentation for urgent treatment in the epidemic. Furthermore, the restrictions imposed due to COVID-19 can impose negative psychological effects, which patients with endometriosis may be more prone to already. In combination with medical therapies, or as an alternative, we encourage patients to consider self-management strategies to combat endometriosis symptoms during the COVID-19 pandemic. These self-management strategies are divided into problem-focused and emotion-focused strategies, with the former aiming to change the environment to alleviate pain, and the latter address the psychology of living with endometriosis. We put forward this guidance, which is based on evidence and expert opinion, for healthcare providers to utilize during their consultations with patients via telephone or video. Patients may also independently use this article as an educational resource. The strategies discussed are not exclusively restricted to consideration during the COVID-19 pandemic. Most have been researched before this period of time and all will continue to be a part of the biopsychological approach to endometriosis long after COVID-19 restrictions are lifted.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".