Healthcare system barriers impacting the care of Canadians with myalgic encephalomyelitis: A scoping review
Bibliographic record
Abstract
BACKGROUND: Myalgic encephalomyelitis (ME, also known as chronic fatigue syndrome or ME/CFS) is a debilitating, complex, multisystem illness. Developing a comprehensive understanding of the multiple and interconnected barriers to optimal care will help advance strategies and care models to improve quality of life for people living with ME in Canada. OBJECTIVES: To: (1) identify and systematically map the available evidence; (2) investigate the design and conduct of research; (3) identify and categorize key characteristics; and (4) identify and analyse knowledge gaps related to healthcare system barriers for people living with ME in Canada. METHODS: The protocol was preregistered in July 2022. Peer-reviewed and grey literature was searched, and patient partners retrieved additional records. Eligible records were Canadian, included people with ME/CFS and included data or synthesis relevant to healthcare system barriers. RESULTS: In total, 1821 records were identified, 406 were reviewed in full, and 21 were included. Healthcare system barriers arose from an underlying lack of consensus and research on ME and ME care; the impact of long-standing stigma, disbelief, and sexism; inadequate or inconsistent healthcare provider education and training on ME; and the heterogeneity of care coordinated by family physicians. CONCLUSIONS: People living with ME in Canada face significant barriers to care, though this has received relatively limited attention. This synthesis, which points to several areas for future research, can be used as a starting point for researchers, healthcare providers and decision-makers who are new to the area or encountering ME more frequently due to the COVID-19 pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.018 | 0.036 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".