The Impact of COVID-19 on Nurse Practitioner Practice and Patient Presentation in Ontario: A Qualitative Study
Bibliographic record
Abstract
Aim: To highlight the impact of the COVID-19 pandemic on nurse practitioner (NP) practice and their patient populations. Background: Across Canada, the pandemic has caused strain on the health care system, health care providers, and patients. Many NPs continued to provide care to their patients via virtual methods as they were not permitted to assess patient in office. Lack of direct physical care was detrimental to both NPs and their patients. Methods: A survey was distributed to 2,094 NPs practicing in Ontario between May and August 2020. After quantitative analysis a qualitative phase was conducted that involved one-on-one semi-structured interviews with 14 NPs who completed the survey and agreed to follow up. Findings: The COVID-19 pandemic has had many impacts on patients and health care workers. Specifically, this study has found that NPs and patients have encountered many obstacles, such as isolation. Additionally, patients have delayed seeking care resulting in various disease progression due to concerns regarding contracting COVID-19. Finally, patients have experienced a decline in their mental health during the pandemic due to factors such as isolation. Conclusion: The effects of the pandemic have been detrimental for both patients and providers as demonstrated in the interviews with NPs in this study. Follow up studies should explore the long-term effects of the 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".