Accessing hemodialysis clinics during the COVID-19 pandemic
Bibliographic record
Abstract
Transportation is a key element of access to healthcare. The COVID-19 pandemic posed unique and unforeseen challenges to patients receiving hemodialysis who rely on three times weekly transportation to receive their life-saving treatments, but there is little data on the problems they faced. This study explores the attitudes, fears, and concerns of hemodialysis patients during the pandemic with a focus on their travel to/from dialysis treatments. A mixed methods travel survey was distributed to hemodialysis patients from three urban centers in Montréal, Canada, during the pandemic (n = 43). The survey included closed questions that were analysed through descriptive statistics as well as open-ended questions that were assessed through thematic analysis. Descriptive statistics show that hemodialysis patients are more fearful of contracting COVID-19 in transit than they are at the treatment center. Patients taking paratransit, public transportation, and taxis are more fearful of COVID-19 while traveling than those who drive, who are driven, or who walk to the clinic. In the open-ended questions, patients reported struggling with confusing COVID-19 protocols in public transport, including conflicting information on whether paratransit taxis allowed one or multiple passengers. Paratransit was the most used travel mode to access treatment (n = 30), with problems identified in the open-ended questions, such as long and unreliable pickup windows, and extended travel times. To limit COVID-19 exposure and stress for paratransit users, agencies should consider sitting one patient per paratransit taxi, clearly communicating COVID-19 protocols online and in the vehicles, and tracking vehicles for more efficient pickups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".