Rehabilitation service utilization among individuals with long COVID in Laval, Canada
Bibliographic record
Abstract
PURPOSE: (1) investigate utilization of rehabilitation services (Physical Therapy [PT], Occupational Therapy [OT], Speech Language Pathology [SLP], and Psychology [Psych]) among adults with long-COVID in Laval, Quebec; (2) determine unmet needs; (3) determine factors associated with receiving services; (4) examine satisfaction. MATERIALS AND METHODS: Descriptive statistics were used to describe rehabilitation services received, reasons for unmet needs, and satisfaction. Bivariate analysis and multivariable logistic regression were used to determine factors associated with receiving services. RESULTS: Of 1031 persons with long-COVID, 37(3.6%) accessed OT, 80(7.8%) PT, 2(0.2%) SLP, and 63(6.1%) Psych. One quarter of participants who did not access rehabilitation services reported needing them. Factors associated with receiving services included hospitalization, vaccination, comorbidities, ≥1 year since COVID-19, female, ≥55 years, married/living together, and unemployed. Reasons for unmet needs were not knowing who to turn to, no referral, and financial. Most were satisfied with the services they received (70-84%). CONCLUSION: The majority of participants with long-COVID did not access rehabilitation services to address their impairments and disabilities. Accessible, multidisciplinary rehabilitation services to address the functional needs of people with long COVID is needed.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".