Characteristics of Canadians Living With Long-Term Health Conditions or Disabilities Who Had Unmet Rehabilitation Needs During the First Wave of the COVID-19 Pandemic
Bibliographic record
Abstract
OBJECTIVE: This study aimed to describe the characteristics associated with unmet rehabilitation needs in a sample of Canadians with long-term health conditions or disabilities during the first wave of the COVID-19 pandemic. DESIGN: We used data from the Impacts of COVID-19 on Canadians Living With Long-Term Conditions and Disabilities, a national cross-sectional survey with 13,487 respondents. Unmet needs were defined as needing rehabilitation (ie, physiotherapy/massage/chiropractic, speech therapy, occupational therapy, counseling services, or support groups) but not receiving due to the pandemic. We used multivariable modified Poisson regression to examine the association between demographic, socioeconomic, and health-related characteristics and unmet rehabilitation needs. RESULTS: More than half of the sample were 50 years and older (52.3%), female (53.8%), and 49.3% reported unmet rehabilitation needs. Those more likely to report unmet needs were females, those with lower socioeconomic status (receiving disability benefits or social assistance, job loss, increased work hours, decreased household income or earnings), and those with lower perceived general health or mental health status. CONCLUSIONS: Among Canadians with disabilities or chronic health conditions, marginalized groups are more likely to report unmet rehabilitation needs. Understanding the systemic and upstream determinants is necessary to develop strategies to minimize unmet rehabilitation needs and facilitate the delivery of equitable rehabilitation services.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".