Does COVID‐19 Related Lockdown Restrictions Impact People With Musculoskeletal Disorders? A Systematic Review
Bibliographic record
Abstract
BACKGROUND: As a result of coronavirus disease 2019 (COVID-19) related lockdown restrictions, people with musculoskeletal (MSK) disorders could be at increased risk of physical and psychological disabilities. This review aimed to summarise the impact of COVID-19 related lockdown restrictions on people with MSK disorders. METHODS: Six electronic databases were searched for studies in the English language published until June 10, 2024. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses to identify, select, and critically appraise relevant research. Two reviewers independently abstracted data from the included studies. Data were summarised using narrative synthesis, and the Newcastle-Ottawa Scale was used for quality assessment. RESULTS: The search strategy identified 637 articles, 129 of which were removed as duplicates. Fifteen studies that met the inclusion criteria were analysed. The sample size the studies reviewed ranged from 40 to 1800. Having MSK disorders during COVID-19 related lockdown restrictions led to increased risk of pain, stress, depression, anxiety, MSK related injuries, decreased quality of life and increased use of emergency department. CONCLUSIONS: This is the first study to report that COVID-19 related lockdown restrictions led to increased risk of pain, MSK injuries and healthcare resource utilisation as well as decreased quality of life among patients with MSK disorders. These results may help inform policy and management strategies in future for people with MSK disorders to mitigate the negative impact of 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 | 0.010 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".