Evaluating Perceptions of Head and Neck Surgeons on the Role of Single-Entry Models in Managing Surgical Waitlists in Ontario: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Long surgical wait times have long plagued health systems in Canada and abroad. This backlog and associated strain on health human resources has been exacerbated by the COVID-19 pandemic, affecting surgeries of varying degrees of urgency across all surgical specialties, including head and neck surgery. Single-entry models (SEMs) are being increasingly studied as one possible strategy to help manage surgical wait times, and a growing number of health systems have implemented SEMs within departments such as otolaryngology-head and neck surgery. We sought to evaluate the views of head and neck surgeons at all 8 designated head and neck cancer centers across Ontario on the role of SEMs in managing surgical backlogs. RESULTS: We interviewed 10 Ontario head and neck surgeons on the role of SEMs in managing wait times within the field. The following themes were elicited from interview transcripts: (1) anticipated positive impact, (2) barriers to implementation, (3) patient experience, and (4) roadmap to implementation. Participants agreed that SEMs may have utility for certain types of surgeries if implemented to address local needs. They also believe this model would have the greatest impact if employed together with other approaches, such as increasing operating room time or nursing availability. CONCLUSION: Our results highlighted the necessity for a nuanced approach to single-entry model implementation in head and neck surgery. While participants recognized the utility of SEMs for high-volume and low-variation surgeries, participants remained divided on the optimal approach to triaging patients necessitating more complex oncologic treatments. Deliberate collaboration among stakeholder organizations and senior surgeons will be critical if SEMs are to succeed in an intricate and political healthcare environment.
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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".