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Record W4213379802 · doi:10.12927/hcq.2022.26717

How were Wait Times for Priority Procedures in Canada Impacted during the First Six Months of the COVID-19 Pandemic?

2022· article· en· W4213379802 on OpenAlexaffvenueabout
Ben Reason, Erin Pichora, Tracy Johnson

Bibliographic record

VenueHealthcare Quarterly · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsCanadian Institute for Health Information
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)PandemicSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakBest practiceMedical emergencyMedicineVirologyPolitical scienceInternal medicineOutbreak

Abstract

fetched live from OpenAlex

In 2020, health systems across Canada responded to the COVID-19 pandemic by making rapid changes to reduce the risk of exposure for patients and staff and to allocate resources toward the treatment of COVID-19 patients. This included postponing surgical and diagnostic procedures. Data collected by the Canadian Institute for Health Information show that these interventions resulted in longer wait times across all provinces in April-September 2020 for scheduled surgical procedures, such as hip and knee replacements and cataract surgeries. The impact on wait times for cancer surgeries and diagnostic imaging varied by type of procedure and jurisdiction, while the wait times for hip fracture repair and radiation therapy were not impacted. Subsequent waves of the COVID-19 pandemic added to the initial backlog of procedures, and it will take time to assess the long-term impact of surgical and diagnostic imaging delays on patient outcomes and wait times.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.149
GPT teacher head0.369
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2022
Admission routes3
Has abstractyes

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