Policy and practices in primary care that supported the provision and receipt of care for older persons during the COVID-19 pandemic: a qualitative case study in three Canadian provinces
Bibliographic record
Abstract
BACKGROUND: The effects of the COVID-19 pandemic on older adults were felt throughout the health care system, from intensive care units through to long-term care homes. Although much attention has been paid to hospitals and long-term care homes throughout the pandemic, less attention has been paid to the impact on primary care clinics, which had to rapidly change their approach to deliver timely and effective care to older adult patients. This study examines how primary care clinics, in three Canadian provinces, cared for their older adult patients during the pandemic, while also navigating the rapidly changing health policy landscape. METHODS: A qualitative case study approach was used to gather information from nine primary care clinics, across three Canadian provinces. Interviews were conducted with primary care providers (n = 17) and older adult patients (n = 47) from October 2020 to September 2021. Analyses of the interviews were completed in the language of data collection (English or French), and then summarized in English using a coding framework. All responses that related to COVID-19 policies at any level were also examined. RESULTS: Two main themes emerged from the data: (1) navigating the noise: understanding and responding to public health orders and policies affecting health and health care, and (2) receiving and delivering care to older persons during the pandemic: policy-driven challenges & responses. Providers discussed their experiences wading through the health policy directives, while trying to provide good quality care. Older adults found the public health information overwhelming, but appreciated the approaches adapted by primary care clinics to continue providing care, even if it looked different. CONCLUSIONS: COVID-19 policy and guideline complexities obliged primary care providers to take an important role in understanding, implementing and adapting to them, and in explaining them, especially to older adults and their care partners.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".