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Record W4316040309 · doi:10.1186/s12889-023-15010-5

Impacts of the COVID-19 pandemic on life and learning experiences of indigenous and non-Indigenous university and college students in Ontario, Canada: a qualitative study

2023· article· en· W4316040309 on OpenAlexafffundabout
Farriss Blaskovits, Imaan Bayoumi, Colleen Davison, Autumn Watson, Eva Purkey

Bibliographic record

VenueBMC Public Health · 2023
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsKingston Health Sciences CentreQueen's University
FundersQueen's UniversityPhysicians' Services Incorporated Foundation
KeywordsPandemicBiostatisticsQualitative researchIndigenousFocus groupPopulationSocial distanceMedicineMedical educationPublic healthQualitative propertyCoronavirus disease 2019 (COVID-19)SociologyNursingSocial science

Abstract

fetched live from OpenAlex

BACKGROUND: The years people spend attending university or college are often filled with transition and life change. Younger students often move into their adult identity by working through challenges and encountering new social experiences. These transitions and stresses have been impacted significantly by the COVID-19 pandemic, which has led to dramatic change in the post-secondary experience, particularly in the pandemic's early months when colleges and universities were closed to in person teaching. The goal of this study was to identify how COVID-19 has specifically impacted the postsecondary student population in Kingston, Ontario, Canada. METHODS: The Cost of COVID is a mixed methods study exploring the social and emotional impacts of the COVID-19 pandemic, with a focus on families, youth, and urban Indigenous People. The present analysis was completed using a subset of qualitative data including Spryng.io micronarrative stories from students in college and university, as well as in-depth interviews from service providers providing services to students. A double-coded phenomenological approach was used to collect and analyze data to explore and identify themes expressed by postsecondary students and service providers who worked with postsecondary students. RESULTS: Twenty-six micronarratives and seven in-depth interviews were identified that were specifically relevant to the post-secondary student experience. From this data, five prominent themes arose. Impacts of the COVID-19 pandemic on the use of technology was important to the post secondary experience. The pandemic has substantial educational impact on students, in what they chose to learn, how it was taught, and experiences to which they were exposed. Health and wellbeing, physical, psychological and emotional, were impacted. Significant impacts were felt on family, community, and connectedness aspects. Finally, the pandemic had important financial impacts on students which affected their learning and their experience of the pandemic. Impacts did differ for Indigenous students, with many of the traditional cultural supports and benefits of spaces of higher education no longer being available. CONCLUSION: Our study highlights important impacts of the pandemic on students of higher education that may have significant individual and societal implications going forward. Both postsecondary institutions and society at large need to attend to these impacts, in order to preserve the wellbeing of graduates, the Canadian labor market, and to ensure that the pandemic does not further exacerbate existing inequalities in post-secondary education in Canada.

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)0.003
metaresearch head score (Gemma)0.004
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: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.446

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0240.010
Scholarly communication0.0050.001
Open science0.0020.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.123
GPT teacher head0.435
Teacher spread0.313 · 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

Citations18
Published2023
Admission routes3
Has abstractyes

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