Recommendations for Recovery of the COVID-19 Pandemic-related Diagnostic, Screening, and Procedure Backlog in Ontario: A Survey of Healthcare Leaders
Bibliographic record
Abstract
PURPOSE: The COVID-19 pandemic has resulted in a significant diagnostic, screening, and procedure backlog in Ontario. Engagement of key stakeholders in healthcare leadership positions is urgently needed to inform a comprehensive provincial recovery strategy. METHODS: A list of 20 policy recommendations addressing the diagnostic, screening and procedure backlog in Ontario were transformed into a national online survey. Policy recommendations were rated on a 7-point Likert scale (strongly agree to strongly disagree) and organized into those retained (≥75% strongly agree to somewhat agree), discarded (≥80% somewhat disagree to strongly disagree), and no consensus reached. Survey participants included a diverse sample of healthcare leaders with the potential to impact policy reform. RESULTS: Of 56 healthcare leaders invited to participate, there were 34 unique responses (61% response rate). Participants were from diverse clinical backgrounds, including surgical subspecialties, medicine, nursing, and healthcare administration and held institutional or provincial leadership positions. A total of 11 of 20 policy recommendations reached the threshold for consensus agreement with the remaining 9 having no consensus reached. CONCLUSION: Consensus agreement was reached among Canadian healthcare leaders on 11 policy recommendations to address the diagnostic, screening, and procedure backlog in Ontario. Recommendations included strategies to address patient information needs on expected wait times, expand health and human resource capacity, and streamline efficiencies to increase operating room output. No consensus was reached on the optimal funding strategy within the public system in Ontario or the appropriateness of implementing private funding models.
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 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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".