A comparative analysis of care seeking behaviors in people living with congestive heart failure during the covid-19 pandemic in the U.S. and U.K.
Bibliographic record
Abstract
Background: The COVID-19 pandemic presented many challenges to persons living with chronic diseases. Patients living with Heart Failure (HF) faced complex challenges due to limitations to access to care due to restrictions associated with the pandemic. The purpose of the study was to examine the self-reported care seeking behaviors of HF patients in the US and UK. The primary aim was to differentiate the ability of HF patients in their respective countries to gain needed services during the pandemic, to examine the structural effects of the vastly different healthcare systems.Methods: A quantitative descriptive design, using an online questionnaire, collected data between May and July 2020 among individuals with HF.Results: US patients reported attending more HF-related appointments than their UK counterparts (p < .001). This is important since UK patients reported a greater likelihood of canceled appointments (p < .05). A greater proportion of US patients reported never having had an appointment canceled compared to those in the UK (p < .05). There were no differences in postponed appointments.Conclusions: Overall, the comparison highlights the extensive availability of specialist services within the US model, contrasting with the UK's system that offers universal access to care.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".