Family physicians’ questions about the COVID-19 pandemic: a content analysis of 2,272 helpline calls
Bibliographic record
Abstract
BACKGROUND: During the COVID-19 pandemic, family physicians faced challenges including travel restrictions for patients, lockdowns, diagnostic testing delays, and changing public health guidelines. Given that 95% of Canadian physicians are members of the Canadian Medical Protective Association (CMPA), the CMPA's telephone helpline - which offers peer-to-peer support - provides valuable insights into family physicians' experiences during the pandemic. METHODS: We used a content analysis approach to identify and understand family physicians' questions and concerns related to the COVID-19 pandemic expressed during calls to the Canadian Medical Protective Association (CMPA) telephone helpline. Calls were classified with preliminary codes and subsequently organized into themes. We collected aggregated data on calls, including province, call date, and whether the physician self-identified having hospital-based activities as part of their practice. Findings from the analysis were explored alongside family physician calls per month (call volume). RESULTS: Between 01 and 2020 and 31 December 2021, 2,272 family physician calls related to the pandemic were included for content analysis. We identified six major themes across these calls: challenging patient interactions; COVID-related care; the impact of the pandemic on the healthcare system; virtual care; physician obligations and rights; and public health matters. COVID-related call volumes were highest early in the pandemic especially among physicians without major hospital affiliation when family physicians practiced with little guidance on how to balance patient care and scarce resources in the face of a novel pandemic. CONCLUSIONS: This research provides unique insight on the effects the COVID-19 pandemic had on family medicine in Canada. These results provide insights on the needs and information gaps of family physicians in a public health crisis and can inform preparedness efforts by public health agencies, professional organizations, educators, and practitioners.
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 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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".