Why Did Home Care Personal Support Service Volumes Drop During the COVID-19 Pandemic? The Contributions of Client Choice and Personal Support Worker Availability
Bibliographic record
Abstract
Home care personal support service delivery decreased during the COVID-19 pandemic, and qualitative studies have suggested many potential contributors to these reductions. This paper provides insight into the source (client or provider) of reductions in home care service volumes early in the pandemic through analysis of a retrospective administrative dataset from a large provider organization. The percentage of authorized services not delivered was 17.2% in Wave 1, 12.6% in Wave 2 and 10.5% in Wave 3, nearing the pre-pandemic baseline of 8.9%. The dominant contribution to reduced home care service volumes was client-initiated holds and cancellations, collectively accounting for 99.3% of the service volume; missed care visits by the provider accounted for 0.7%. Worker availability also declined due to long-term absences (which increased 5-fold early in Wave 1 and remained 4× above baseline in Waves 2 and 3); short-term absences rose sharply for 6 early-pandemic weeks, then dropped below the pre-pandemic baseline. These data reveal that service volume reductions were primarily driven by client-initiated holds and cancellations; despite unprecedented decreases in Personal Support Worker availability, missed care did not increase, indicating that the decrease in demand was more substantial and occurred earlier than the decrease in worker availability.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".