Perceptions of Ontario health system leaders on single-entry models for managing the COVID-19 elective surgery backlog: an interpretive descriptive study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has exacerbated pre-existing challenges with respect to access to elective surgery across Canada, and a single-entry model (SEM) approach has been proposed as an equitable and efficient method to help manage the backlog. With Ontario's recent investment in centralized surgical wait-list management, we sought to understand the views of health system leaders on the role of SEMs in managing the elective surgery backlog. METHODS: We used the qualitative method of interpretive description to explore participant perspectives and identify practical strategies for policy-makers, administrators and clinical leaders. We conducted semistructured interviews with health system leaders from across Ontario on Zoom between March and June 2021. We used snowball and purposive sampling. Inclusion criteria included Ontario health care leaders, fluent in English or French, in positions relevant to managing the elective surgery backlog. Exclusion criteria were individuals who work outside Ontario, or do not hold relevant roles. RESULTS: Our interviews with 10 health system leaders - including hospital chief executive officers, surgeons, administrators and policy experts - resulted in 5 emergent domains: perceptions of the backlog, operationalizing and financing SEMs, barriers, facilitators, and equity and patient factors. All participants emphasized the need for clinical leaders to champion SEMs and the utility of SEMs in managing wait-lists for high-volume, low-acuity, low-complexity and low-variation surgeries. INTERPRETATION: Although SEMs are no panacea, the participants in our study stated that they believe SEMs can improve quality and reduce variability in wait times when SEMs are designed to address local needs and are implemented with buy-in from champions. Health care leaders should consider SEMs for improving surgical backlog management in their local jurisdictions.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".