Lessons from experiences of accessing healthcare during the pandemic for remobilizing rheumatology services: a national mixed methods study
Bibliographic record
Abstract
Objectives: To understand the impact of the coronavirus disease 2019 pandemic on access to healthcare services for patients with inflammatory and non-inflammatory musculoskeletal (MSK) conditions. Methods: Three established cohorts that included individuals with axial SpA, psoriatic arthritis and MSK pain completed a questionnaire between July and December 2020. In parallel, a subset of individuals participated in semistructured interviews. Results: A total of 1054 people (45% female, median age 59 years) were included in the quantitative analyses. Qualitative data included 447 free-text questionnaire responses and 23 interviews. A total of 57% of respondents had tried to access care since the start of the UK national lockdown. More than a quarter reported being unable to book any type of healthcare appointment. General practice appointments were less likely to be delayed or cancelled compared with hospital appointments. Younger age, unemployment/health-related retirement, DMARD therapy, anxiety or depression and being extremely clinically vulnerable were associated with a greater likelihood of attempting to access healthcare. People not in work, those reporting anxiety or depression and poorer quality of life were less likely to be satisfied with remotely delivered healthcare. Participants valued clear, timely and transparent care pathways across primary care and specialist services. While remote consultations were convenient for some, in-person appointments enabled physical assessment and facilitated the development and maintenance of clinical relationships with care providers. Conclusions: We identified patient factors that predict access to and satisfaction with care and aspects of care that patients value. This is important to inform remobilisation of rheumatology services to better meet the needs of patients.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".