MétaCan
Menu
Back to cohort
Record W4284975247 · doi:10.2196/39404

Development and Implementation of Ontario Critical Care Clinical Practice Rounds

2022· article· en· W4284975247 on OpenAlexaffvenueabout
Zoya Adeel, Neill K. J. Adhikari, Josée Theriault, Bernard Lawless, Maria Cheung, Lynn Ward, Michael Sullivan, David Neilipovitz

Bibliographic record

VenueIproceedings · 2022
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsOttawa HospitalSt. Michael's HospitalToronto General HospitalCARE CanadaHealth Sciences NorthUniversity of TorontoSunnybrook HospitalHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsHealth carePandemicPublic relationsKnowledge translationPresentation (obstetrics)Best practiceNursingCoronavirus disease 2019 (COVID-19)MedicineMedical educationPolitical sciencePsychologyKnowledge managementComputer science

Abstract

fetched live from OpenAlex

Background The COVID-19 pandemic brought unprecedented challenges to health care systems across the world. Health care professionals were burdened with time constraints as they balanced care for a large number of patients while managing crippling resource shortages. In the pandemic’s early stages, it was challenging for health care providers to provide evidence-based therapies due to the novel nature of COVID-19. There were also pressures to adopt unproven, yet highly touted treatments based on media reports and social media postings. These challenges were identified by Critical Care Services Ontario (CCSO), a provincial health organization that ensures the integration of the critical care system in Ontario, Canada. Since traditional methods of knowledge translation were inaccessible during the pandemic, CCSO created a webinar series titled Ontario Critical Care Clinical Practice Rounds (OC3PR) to share evidence-based practices with critical care professionals. Objective We sought to develop and implement a webinar series to connect critical care professionals with the best available evidence and clinical expertise during the COVID-19 pandemic. We were also interested in gathering attendee perceptions of OC3PR as an educational tool. Methods CCSO collaborated with 5 regional critical care leaders in Ontario to develop and implement OC3PR. This committee identified presentation topics based on perceived urgency and demand and selected presenters with expertise on their respective discussion topic. To promote accessibility, OC3PR was facilitated on the Zoom platform, live simulcasted on Youtube, and subsequently posted on Youtube for asynchronous viewing. Attendees also had the opportunity to share inquiries in live questions-and-answers sessions facilitated by the presenters. Finally, to gather the perceptions of and experiences with OC3PR, we invited attendees to partake in a web-based questionnaire at the end of each session. Results In total, 19 webinars were presented from November 26, 2020, to December 2, 2021, with 1481 registered unique attendees from within Canada and internationally and 17,533 Youtube visits. OC3PR presentation topics centered on resource rationing, patient therapies, staffing challenges, infection control, and vaccination. In addition, 22 follow-up questionnaires yielded 408 responses from attendees, which were composed of physicians (32%), registered nurses (15%), and other health care professionals. Our survey results suggest that OC3PR is beneficial to professionals as the majority of the respondents strongly agreed that it was of acceptable quality, enhanced their knowledge, relevant to their practice, and allotted appropriate timing for interactive components. Most (98%) respondents also reported that they would attend another OC3PR session. Conclusions The success of OC3PR as an accessible educational tool made it evident to CCSO that it would continue this forum in a postpandemic context. Although the webinar series was created for critical care professionals, it can be adapted by other health organizations to improve the integration of their health networks and enhance the support they provide for their workers. Conflicts of Interest None declared.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.540
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.126
GPT teacher head0.510
Teacher spread0.385 · 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

Citations0
Published2022
Admission routes3
Has abstractyes

Explore more

Same venueIproceedingsSame topicCOVID-19 and healthcare impactsFrench-language works237,207