Patient perspectives of a fall leading to emergency department visit during the COVID-19 pandemic: A qualitative study in Northern Ontario
Bibliographic record
Abstract
Background and objectives Falls are a leading cause of injury-related emergency department (ED) visits and hospitalizations across Canada and can result in decline in function and quality of life for an older adult. The COVID-19 pandemic placed severe strain on EDs and hospitals across Canada. This study aimed to understand the experience of older adults who presented to the ED as a result of having a fall during a COVID-19 surge. This may help generate hypotheses and ideas about how to provide optimal care in the midst of pandemic-related healthcare system disruption. Methods A purposive sampling strategy identified nine participants over age 65 in a medium population centre in Northern Ontario, Canada. Qualitative semi-structured interviews sought information regarding patient demography and function prior to their fall, the circumstances and communication surrounding their fall, and information about their post-fall recovery. Qualitative thematic analysis of the data was completed. Results Three main themes were identified: patients minimized their fall risk; the challenge of interacting with the healthcare system in a time of limited resources; and complex care navigation and fragmentation within the healthcare system. Discussion Participants were uncertain about the significance of a fall. Post-fall care was directed by the participant, and little coordination or education was provided at the point of contact with the health care system. Participants were hesitant to present to the ED. Participants expressed vulnerability when reflecting on their experience and acknowledged the systemic pressures experienced by their healthcare workers during the COVID-19 pandemic.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.022 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".