What Drove Clients’ Decisions to Pause Personal Homecare Services Before and During the Pandemic?
Bibliographic record
Abstract
Background: With an increasing reliance on homecare and a scarcity of providers, there is potential to gain insight from existing administrative data to optimize planning and care delivery. To enable more accurate predictions of service use, it is important to understand the degree to which various factors influence clients' difficult decisions to temporarily pause their receipt of necessary homecare services. Objectives: We utilized a large, longitudinal, administrative dataset to examine the relative effects of client-level factors on the outcomes of (1) placing a hold on homecare services and (2) the length of a homecare service hold, through stratified regression analyses separated by pre-, early-, and mid-pandemic periods. Design: Descriptive summaries of the samples consisted of graphical representation and frequencies (proportions) or means. The relationship between client sociodemographic and homecare utilization factors on the service hold initiation and length were evaluated using mixed-effects logistic and linear regression, respectively, stratified by pre-, early-, and mid-pandemic periods. Odds ratios (OR) for hold initiation and exponentiated estimates for hold length were calculated with corresponding 95% confidence intervals. Results: Findings provide a better understanding of the decisions made by a large sample of homecare clients to pause their homecare services in pre-, early-, and mid-pandemic scenarios. Frequency and length of service holds more than doubled in the early-pandemic period; although hold frequencies then returned to pre-pandemic rates, hold durations remained slightly longer. There were notable differences over time, but generally, clients with higher care needs had a reduced likelihood of placing a hold on homecare services. Shorter homecare tenure and previously cancelling individual homecare visits were also good indicators of future service decisions. Conclusion: Findings are relevant for organizations providing homecare services, policymakers, and those interested in predicting homecare utilization for resource allocation planning with the goal of optimal care delivery.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".