Improving Cancer Diagnosis in Alberta, Canada: A Qualitative Study of Emergency Department Healthcare Providers’ Perspectives on Diagnosing Cancer in the Emergency Setting
Bibliographic record
Abstract
Cancer is the leading cause of death in Canada, with diagnoses increasing annually. In Alberta, many cancer cases are detected in emergency departments, often at advanced stages. Despite the significant role of emergency departments in cancer diagnosis, limited research exists on the experiences of healthcare providers in this context. This qualitative study aimed to explore the perspectives of physicians and nurses working in emergency departments in Edmonton and Calgary regarding cancer diagnosis. Semi-structured interviews were conducted with 17 physicians and nurses, recruited through convenience and snowball sampling. Data collection continued until thematic saturation was reached. Interviews were analyzed thematically using an inductive, iterative process. Three main themes emerged: the acute care focus of the emergency department, its unsuitability for cancer diagnosis, and the need for systemic improvements to better support patients with suspected cancer. Participants highlighted challenges related to high patient volumes, the emotional burden of delivering cancer diagnoses, and barriers to effective communication and patient interaction in a fast-paced, high-pressure environment. The findings suggest the need for systemic reforms, including stronger primary care and improved care coordination, to alleviate pressure on emergency departments and enhance both patient outcomes and healthcare provider well-being.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".