Provider and Patient Experiences of Delays in Primary Care During the Early COVID-19 Pandemic
Bibliographic record
Abstract
BACKGROUND: The necessary suspension of nonacute services by healthcare systems early in the COVID-19 pandemic was predicted to cause delays in routine care in the United States, with potentially serious consequences for chronic disease management. However, limited work has examined provider or patient perspectives about care delays and their implications for care quality in future healthcare emergencies. OBJECTIVE: This study explores primary care provider (PCP) and patient experiences with healthcare delays during the COVID-19 pandemic. METHODS: PCPs and patients were recruited from four large healthcare systems in three states. Participants underwent semistructured interviews asking about their experiences with primary care and telemedicine. Data were analyzed using interpretive description. RESULTS: Twenty-one PCPs and 65 patients participated in interviews. Four main topics were identified: (1) types of care delayed, (2) causes for delays, (3) miscommunication contributing to delays, and (4) patient solutions to unmet care needs. CONCLUSIONS: Both patients and providers reported delays in preventive and routine care early in the pandemic, driven by healthcare system changes and patient concerns about infection risk. Primary care practices should develop plans for care continuity and consider new strategies for assessing care quality for effective chronic disease management in future healthcare system disruptions.
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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".