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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".