“Doing extra work and not getting extra help”: the burden of work generated to manage the “no visitors” policy during the COVID-19 pandemic in Ontario, Canada
Bibliographic record
Abstract
Background: During the pandemic, a "no visitors" policy was implemented across hospitals in Ontario, Canada. Without caregivers present in-hospital to support patient care (e.g., treatment decision-making, advocacy, treatment compliance, social support), there was a perceived decline in care quality. Despite existing research on the extra work required to navigate the loss of caregiver support in-hospital, there is a paucity of understanding about the work required to manage the "no visitors" policy itself-including creative ways to work around it. This qualitative research study draws attention to the "no visitors" policy and the work to manage and work around these limitations across healthcare system silos (drawing on cancer care and alternate level of care as case examples) in Ontario, Canada. Methods: In total, 5 focus groups and 53 interviews were conducted with 68 participants (10 patients, 7 caregivers, 40 healthcare providers, and 11 healthcare decision-makers). The authors engaged in codebook thematic analysis. Findings: Managing the "no visitors" policy and pushback against it generated a significant burden of work for patients, caregivers, healthcare providers, and healthcare decision-makers at a time when difficult emotions were high and resources and capacity were low. Five themes are discussed that depict the burden of work: (1) work of making individual exceptions to the "no visitors" policy, (2) work of standardizing exceptions, (3) work to remedy and navigate inconsistencies across hospital units or partner organizations, (4) workarounds to gain in-hospital entry via "hot words" and sneaking in, and (5) workarounds when in-hospital entry was not possible via technology and visiting through windows. Conclusion: The denial of caregivers' entry into hospitals during the COVID-19 pandemic undermined their value as essential care partners, despite their contributions to patient care. Unintended consequences of such public health policy, including the generation of burdensome work to manage and work around it for all involved, must therefore be more carefully considered for future pandemic preparedness.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.041 | 0.024 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".