The impact of the COVID-19 pandemic on primary care physicians and nurses in Nova Scotia: a qualitative exploratory study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has brought immense disruption worldwide, dramatically altering the ways we live, work and learn on a day-to-day basis; however, few studies have investigated this from the perspective of primary care providers. In this study, we sought to explore the experiences of primary care providers in the province of Nova Scotia, with the intention of understanding the impact of the COVID-19 pandemic on primary care providers' ability to provide care, their information pathways, and the personal and professional impact of the pandemic. METHODS: We conducted an exploratory qualitative research study involving semistructured interviews conducted via Zoom videoconferencing or telephone with primary care providers (physicians, nurse practitioners and family practice nurses) who self-identified as working in primary health care in Nova Scotia from June 2020 to April 2021. We performed a thematic analysis involving coding and classifying data according to themes. Emergent themes were then interpreted by seeking commonalties, divergence, relationships and overarching patterns in the data. RESULTS: Twenty-four primary care providers were interviewed. Subsequent analysis identified 4 interrelated themes within the data: disruption to work-life balance, disruptions to "non-COVID-19" patient care, impact of provincial and centralized policies, and filtering and processing an influx of information. INTERPRETATION: Our findings showed that managing a crisis of this magnitude requires coordination and new ways of working, balancing professional and personal life, and adapting to already implemented changes (i.e., virtual care). A specific primary care pandemic response plan is essential to mitigate the impact of future health care crises.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".