Physician questions and concerns related to COVID-19: a content analysis of advice calls to a medico-legal helpline
Bibliographic record
Abstract
BACKGROUND: With the onset of the COVID-19 pandemic, physicians have had concerns related to the impact of the pandemic on their practice of medicine. Our objective was to evaluate physician questions and concerns related to the COVID-19 pandemic by studying physician calls made to a medico-legal telephone helpline, and explore associations between the pattern of these calls and the temporal progression of the pandemic. METHODS: We conducted a descriptive study of calls related to the COVID-19 pandemic to the Canadian Medical Protective Association (CMPA) from Jan. 1, 2020, to June 30, 2021. Using content analysis, we classified calls into themes. Using a Poisson regression model, we tested for associations between the weekly numbers of physician calls related to COVID-19 and national rates of COVID-19 cases and deaths. RESULTS: = 0.002) but not across the entire study period. Call themes included virtual care (826 calls), the pandemic's effect on health care (1160 calls) and challenging patient interactions (1091 calls). INTERPRETATION: We observed high volumes of physician calls to a medico-legal helpline during the first 18 months of the COVID-19 pandemic in Canada. Our data provide insight into the questions and concerns of Canadian physicians, and serve as a contemporaneous account of the adaptability and resilience of physicians during this challenging time.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.001 | 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".