Perceptions of Otolaryngologists on Single-Entry Models for Managing Wait Times in Community-Based Health Care in Ontario: A Qualitative Study
Bibliographic record
Abstract
ImportanceLong wait times for medical care have been exacerbated following the pandemic in many health systems. Single-entry models (SEMs) have been proposed as a strategy to manage growing surgical backlogs and increase timeliness and quality of care by creating a single queue and centralizing the referral triage process.ObjectiveThe primary objective was to evaluate the perceptions of SEMs among community otolaryngologists for managing surgical backlogs. The secondary objectives were to better understand their experiences with the current system and to investigate their recommendations for implementing an SEM.DesignInterpretive Description.SettingOntario, Canada.ParticipantsNine community-based otolaryngologists.Intervention/ExposuresNot available.MethodsVirtual semi-structured interviews were conducted with study participants. Data were independently analyzed using inductive and deductive methods by multiple team members. Results were triangulated, and a final coding framework was developed collaboratively from which themes were identified.Main Outcome MeasuresPerceptions of SEMs as well as recommendations for design and implementation.ResultsThree thematic domains and 9 subdomains were identified from our interview data: (1) factors affecting the utility of SEMs; (2) opinions and buy-in of physicians; and (3) opportunities to improve equity.Conclusions and RelevanceWe identified a number of factors that should be considered in supporting community-based otolaryngologists to adopt SEMs as a strategy for ensuring timely and equitable access to care. Clinical leaders and specialty organizations play a pivotal role for such changes to succeed. Implementing SEMs may be an important step toward increasing equity, quality, efficiency, and cost-effectiveness in otolaryngology.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| 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".