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Record W4385495435 · doi:10.1080/02673843.2023.2239327

“The most important thing is to communicate with students”: experiences and voices of Canadian youth during the COVID-19 pandemic

2023· article· en· W4385495435 on OpenAlexafffundabout
Negin A. Riazi, Jessica Goddard, Sarah Lappin, Valerie Michaelson, Terrance J. Wade, Karen A. Patte

Bibliographic record

VenueInternational Journal of Adolescence and Youth · 2023
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsBrock University
FundersInstitute of Human Development, Child and Youth HealthCanadian Institutes of Health ResearchHospital for Sick Children
KeywordsPandemicModalitiesCoronavirus disease 2019 (COVID-19)PsychologyMental health2019-20 coronavirus outbreakMedical educationFocus groupSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Public relationsMedicinePolitical scienceSociologyPsychiatry

Abstract

fetched live from OpenAlex

This study aimed to learn directly from youth about how they navigated and experienced the COVID-19 pandemic response, with a focus on secondary school policies and protocols. Thirty semi-structured one-on-one interviews were conducted with Canadian youth (13–18 years old, 53.3% girls, 46.7% white) and analysed using inductive interpretive description. Youth discussed challenges related to a lack of direct communication and consultation about pandemic-related decisions, the shifts between different school modalities, the loss of extracurricular opportunities, and a need for mental health support, which they connected to adverse impacts on their learning, health, and future opportunities. Participants’ top recommendation for adults was to include youth in decision-making on matters that impact them. To uphold their rights, support healthy development, and ensure more effective policies/protocols, the authentic engagement of youth in decision-making processes and improved communication are necessary and were absent during the pandemic.

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)0.000
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 score0.274
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.087
GPT teacher head0.414
Teacher spread0.327 · 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

Citations5
Published2023
Admission routes3
Has abstractyes

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