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Record W4414411509 · doi:10.3390/youth5030099

How Tuition Waivers and Holistic Supports Foster Success in Post-Secondary Education Among Care-Experienced Youth

2025· article· en· W4414411509 on OpenAlexafffundabout
Dale Kirby, Jacqueline Gahagan, Steven M. Smith, Kristyn Anderson, Sue McWilliam, Rasnat Chowdhury

Bibliographic record

VenueYouth · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicChild Welfare and Adoption
Canadian institutionsIzaak Walton Killam Health CentreUniversity of TorontoDalhousie UniversitySaint Mary's UniversityMount Saint Vincent UniversityMemorial University of Newfoundland
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchMount Saint Vincent University
KeywordsWaiverSocioeconomic statusVariety (cybernetics)Mental healthFace (sociological concept)Academic achievementHigher education

Abstract

fetched live from OpenAlex

Youth transitioning to post-secondary education in Canada face a variety of barriers, but care-experienced youth (CEY) can encounter distinct barriers, including financial insecurity, social isolation, and a lack of academic preparation. This paper explores how tuition waiver programs contribute to CEY student success by alleviating financial burden and facilitating access to higher education. Drawing on an international scoping review and interviews with CEY and support professionals, our research highlights key components of tuition waiver programs that enhance student retention, persistence, engagement, academic achievement, and ultimately graduation. Our findings underscore the necessity of holistic wraparound supports—such as mentorship, mental health services, and academic advising—to ensure successful transitions for CEY and improve their long-term educational and socioeconomic outcomes.

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)0.000
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.151
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.013
GPT teacher head0.283
Teacher spread0.270 · 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 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

Citations0
Published2025
Admission routes3
Has abstractyes

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