MétaCan
Menu
Back to cohort
Record W3089806609 · doi:10.1177/2333794x20957652

Perceived Impacts of the COVID-19 Pandemic on Pediatric Care in Canada: A Roundtable Discussion

2020· article· en· W3089806609 on OpenAlexafffundabout
David Nicholas, Mark Belletrutti, Gina Dimitropoulos, Sherri L. Katz, Adam Rapoport, Simon Urschel, Lori J. West, Lonnie Zwaigenbaum

Bibliographic record

VenueGlobal Pediatric Health · 2020
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsUniversity of TorontoUniversity of CalgaryChildren's Hospital of Eastern OntarioUniversity of OttawaHospital for Sick ChildrenStollery Children's HospitalUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsPandemicMedicineCoronavirus disease 2019 (COVID-19)Health careMental healthFamily medicineMEDLINENursingPsychiatry

Abstract

fetched live from OpenAlex

Like other recipients of health care services, pediatric patients and their families/caregivers have been profoundly impacted by health care shifts and broader societal restrictions associated with the COVID-19 pandemic. An online roundtable discussion was facilitated with 7 pediatric clinicians and investigators of a current study examining the impacts of COVID-19 on pediatric care at multiple Canadian sites. Discussants represented a range of pediatric specialities: developmental disability, mental health, cardiac transplantation, respiratory medicine, hematology, and palliative care. We offer the transcript of the roundtable in which discussants reflected on clinical and programmatic experiences of the pandemic, including perceived impacts on children receiving care and their families, potential opportunities for improved health care delivery, impacts on health care providers, and recommendations as we move toward easing restrictions and pandemic recovery. Discussants convey a range of considerations that may have varying relevance for pediatric specialities in terms of practice and program planning.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
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 head0.053
GPT teacher head0.352
Teacher spread0.299 · 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

Citations17
Published2020
Admission routes3
Has abstractyes

Explore more

Same venueGlobal Pediatric HealthSame topicChildhood Cancer Survivors' Quality of LifeFrench-language works237,207