Improving colorectal cancer in Alberta, Canada: a qualitative study of patients and close contacts’ perceptions on diagnosis following an emergency department presentation
Bibliographic record
Abstract
BACKGROUND: Colorectal cancer (CRC) is globally the third most prevalent cancer and a leading cause of cancer-related deaths. In Alberta, Canada, a significant portion of CRC diagnoses occur following emergency department (ED) presentations. Gaps remain in understanding patient's perspectives on CRC diagnosis after an ED visit. The aim of this study was to examine the experiences and perspectives of a group of patients diagnosed with CRC subsequent to an ED visit in Alberta and their close contacts. METHODS: We conducted a qualitative study using in-depth, semi-structured interviews with patients diagnosed with CRC after an ED visit at the Rockyview General Hospital, Calgary, and their close contacts, from November 2022 to June 2023. Interviews focused on symptom recognition, healthcare interactions, and the decision-making process leading to an ED visit. They were conducted in-person or over the phone, and analysed using thematic analysis. RESULTS: Eighteen participants (12 patients and 6 close contacts) were interviewed, revealing four main themes: (1) variability in symptom recognition and interpretation; (2) inconsistencies in primary care consultations; (3) factors influencing decision-making leading to an ED visit; and (4) recommendations for expedited diagnosis outside of EDs. CONCLUSION: The findings highlight the complexity of the diagnostic journey for CRC patients in Alberta, pointing to significant gaps in symptom recognition and response by patients and healthcare providers. Improved diagnostic protocols and targeted support for healthcare providers, as well as approaches to address systemic delays may help streamline the diagnostic journey. Future research should focus on exploring innovative interventions to address the identified barriers to timely CRC diagnosis.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".