Surgeon perspectives on surgical wait times and the single-entry model: challenges and opportunities for equitable access in a Canadian health care system
Bibliographic record
Abstract
BACKGROUND: Surgical wait times affect patient outcomes, access to care, and surgeon well-being. The single-entry model (SEM) has been proposed to address these issues, but its implementation raises concerns among surgeons. We sought to explore surgeons' perspectives on surgical wait times, referral processes, and the potential effect of the SEM on improving access to surgical care in a major metropolitan Canadian city. METHODS: We conducted a qualitative descriptive study to explore the perspectives of surgeons at a large community hospital using semi-structured interviews. We used thematic analysis to identify key themes regarding surgical wait times, referral processes, and the SEM. RESULTS: We interviewed 10 surgeons with different specialties and administrative roles, and a median 16.5 years of experience. Participants expressed frustration with systemic inefficiencies, particularly regarding long wait times for consultations and surgeries. Key issues included overwhelming workloads, limited operating room availability, and nonspecific referrals, which often led to delayed patient care. Surgeons reported burnout from managing these delays. Although many participants viewed the SEM as a promising strategy to improve equitable access to care, others raised concerns about depersonalization of care and reduced surgeon autonomy. Some participants emphasized that, without concurrent reforms to funding models, the SEM could inadvertently reinforce existing disparities, particularly gender-based pay inequities. CONCLUSION: Surgical wait times pose substantial challenges for both patient outcomes and surgeon well-being. The SEM holds promise for reducing delays and improving equity in patient access, but its successful implementation requires addressing concerns related to surgeon autonomy and workforce equity.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".