Humans not heroes: Canadian emergency physician experiences during the early COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: The pandemic has upended much clinical care, irrevocably changing our health systems and thrusting emergency physicians into a time of great uncertainty and change. This study is a follow-up to a survey that examined the early pandemic experience among Canadian emergency physicians and aimed to qualitatively describe the experiences of these physicians during the global pandemic. The study was conducted at a time when Canadian COVID-19 case numbers were low. METHODS: The investigators engaged in an interview-based study that used an interpretive description analytic technique, sensitised by the principles of phenomenology. One-to-one interviews were conducted, transcribed and then analysed to establish a codebook, which was subsequently grouped into key themes. Results underwent source triangulation (with survey data from a similar period) and investigator-driven audit trail analysis. RESULTS: A total of 16 interviews (11 female, 5 male) were conducted between May and September 2020. The isolated themes on emergency physicians' experiences during the early pandemic included: (1) disruption and loss of emergency department shift work; (2) stress of COVID-19 uncertainty and information bombardment; (3) increased team bonding; (4) greater personal life stress; (5) concern for patients' isolation, miscommunication and disconnection from care; (6) emotional distress. CONCLUSIONS: Canadian emergency physicians experienced emotional and psychological distress during the early COVID-19 pandemic, at a time when COVID-19 prevalence was low. This study's findings could guide future interventions to protect emergency physicians against pandemic-related distress.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".