Exploring the impact of the COVID-19 pandemic on physician complaint trends in Alberta, Canada
Bibliographic record
Abstract
Background The COVID-19 pandemic has posed unprecedented challenges to healthcare systems globally. This study aimed to investigate the pandemic's impact on physician complaints in Alberta, Canada, across three periods: pre-, during, and post-pandemic. Methods A cross-sectional study was conducted using the College of Physicians & Surgeons of Alberta (CPSA) complaint database from Q1 2018 to Q4 2023. Complaints were grouped into eight domains, with a detailed analysis of COVID-19-related complaints. A qualitative analysis categorized complaints into thematic domains, and trends were analyzed. The frequency of complaints was compared by using the chi-square test. Results Complaint trends showed stability pre-pandemic, decreasing during the pandemic, and rising post-COVID. Medical reporting complaints decreased during the pandemic but increased post-COVID. Conversely, practice management, third-party involvement, ethical concerns, and systemic issue complaints increased during the pandemic and continued into the post-COVID period. COVID-19-related complaints peaked in Q1 2022, with family physicians receiving most of them. Conclusions Proactive monitoring of complaint trends is essential for healthcare organizations to ensure high-quality, patient-centered care. This study provided insights into the impact of the COVID-19 pandemic on physician complaints in Alberta, emphasizing the necessity for continuous monitoring and intervention.
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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 | 0.000 |
| 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 | 0.001 |
| 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".