Delays, Delays, Delays” – Family Physicians’ Experiences Managing Patients and Accessing Specialists During COVID-19
Bibliographic record
Abstract
Context: Historically, Canadians experience long wait times for specialist referrals. During the COVID-19 pandemic, many specialist services were suspended to reduce virus transmission and redeploy physicians. Little has been reported on family physicians’ (FPs’) perspectives and experiences accessing specialists during the pandemic and implications for practice. Objective: To understand FPs’ experiences accessing specialists during the COVID-19 pandemic. Study design and analysis: A qualitative, multiple case study was conducted. Relevant data (i.e., pertaining to specialists) were thematically analyzed. Setting: Four Canadian jurisdictions (Vancouver Coastal Health region, British Columbia; Southwestern Ontario; the province of Nova Scotia; and Eastern Health region of Newfoundland and Labrador). Population studied: FPs who were practicing in Canada during the COVID-19 pandemic. Instrument: Semi-structured exploratory qualitative interviews. Outcome measures: Perspectives from FPs on accessing specialists during the COVID-19 pandemic. Results: Thirty-nine of 68 FP interviewees described experiences accessing specialists during the pandemic. Findings were organized according to the CanMEDS competency framework. This framework describes competencies held by FPs and, by organizing identified themes by this framework, we illustrate how FPs applied these competencies during the pandemic. Themes included: 1) collaborator: barriers and enabler to accessing specialists, opportunities for greater collaboration, and conflict between specialists and FPs; 2) medical expert: working beyond typical scope to manage patients in lieu of specialist appointments; 3) health advocate: identifying patient needs and advocating for them to be seen by specialists; 4) communicator: explaining changes to patients and managing patient frustration; 5) scholar: integrating best practices and contributing to research; 6) professional: applying best practices and identifying issues of FP wellness; 7) leader: suggesting improvements to challenges exacerbated by the pandemic. Conclusion: The COVID-19 pandemic exacerbated existing challenges in primary healthcare. Although there were challenges associated with the lack of access to specialists, and FPs took on additional work to address access challenges, there were also opportunities for greater collaboration and innovation.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".