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Record W3110294196 · doi:10.1186/s40900-020-00242-1

Family perspectives of COVID-19 research

2020· letter· en· W3110294196 on OpenAlexaff
Shelley Vanderhout, Catherine S. Birken, Peter Wong, Sarah A. Kelleher, Shannon Weir, Jonathon L. Maguire

Bibliographic record

VenueResearch Involvement and Engagement · 2020
Typeletter
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsCanadian Association for Theatre ResearchSickKids FoundationHospital for Sick ChildrenSt. Michael's Hospital
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Relevance (law)PandemicMultidisciplinary approachPsychology2019-20 coronavirus outbreakRecreationData collectionValue (mathematics)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Medical educationMedicineSociologyPolitical scienceDiseaseSocial science

Abstract

fetched live from OpenAlex

BACKGROUND: The COVID-19 pandemic has uniquely affected children and families by disrupting routines, changing relationships and roles, and altering usual child care, school and recreational activities. Understanding the way families experience these changes from parents' perspectives may help to guide research on the effects of COVID-19 among children. MAIN BODY: As a multidisciplinary team of child health researchers, we assembled a group of nine parents to identify concerns, raise questions, and voice perspectives to inform COVID-19 research for children and families. Parents provided a range of insightful perspectives, ideas for research questions, and reflections on their experiences during the pandemic. CONCLUSION: Including parents as partners in early stages of COVID-19 research helped determine priorities, led to more feasible data collection methods, and hopefully has improved the relevance, applicability and value of research findings to parents and children.

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 imitation

Not 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.

metaresearch head score (Codex)0.028
metaresearch head score (Gemma)0.041
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.041
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0200.011
Scholarly communication0.0060.006
Open science0.0010.007
Research integrity0.0060.010
Insufficient payload (model declined to judge)0.0060.001

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 head0.619
GPT teacher head0.569
Teacher spread0.051 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations34
Published2020
Admission routes1
Has abstractyes

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