On‐Demand Schedules, Worker Absenteeism and Patient Dissatisfaction in Home Care Services
Bibliographic record
Abstract
ABSTRACT Service companies often adopt on‐demand scheduling to balance labor costs and fluctuating market demand. However, research shows that such practices can reduce worker productivity and retention. In this study, we examine how on‐demand scheduling affects two critical outcomes: worker absenteeism and patient dissatisfaction. We extend the conceptualization of undesirable scheduling by introducing schedule discontinuity —the presence of unpaid interruptions within a worker's daily schedule—alongside the more commonly studied schedule inconsistency , or variability in work hours across weeks. Using data on 1.2 million home care visits in a Canadian healthcare provider, we find that both schedule inconsistency and discontinuity significantly increase absenteeism and patient dissatisfaction. Specifically, moving from the 25th to 75th percentile in discontinuity (inconsistency) raises absenteeism by 20.00% (19.29%), and customer complaints by 27.33% (40.27%). To assess the practical implications for employers, we formulate and solve a schedule optimization problem that minimizes schedule discontinuity (or inconsistency), while satisfying demand and supply constraints. Applying a machine learning predictive model to these optimized schedules, we estimate reductions in the probability of absenteeism by 9.5% (8.2%) and in the probability of patient complaints by 7.7% (2.3%), demonstrating that modest scheduling adjustments can substantially improve worker and service outcomes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".