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Record W2168886752 · doi:10.47678/cjhe.v42i3.2098

Use of Campus Support Services by Ontario College Students

2012· article· en· W2168886752 on OpenAlexaffvenueabout
Peter Dietsche

Bibliographic record

VenueCanadian Journal of Higher Education · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedical educationEthnic groupService (business)PsychologyReferralService delivery frameworkHigher educationPublic relationsBusinessSociologyMarketingPolitical scienceMedicineNursing

Abstract

fetched live from OpenAlex

Offering an array of support services to meet the diverse needs of post-secondary learners assumes that these services improve success by providing students with compensatory resources and opportunities for engagement (Purnell & Blank, 2004). Little Canadian research, however, has examined students’ use of support services. This study describes how campus support services are used by Ontario college students and factors that influence the uptake of those services. Results show that despite relatively high student-reported need, the majority of Ontario college students did not utilize most campus services. Age, gender and ethnicity, receptivity to support, negative college experiences, faculty referral, studying with peers, and poor grades were associated with increased use of some services. The findings argue for a proactive service delivery model using web-based resources to minimize location-based barriers and to more effectively promote services dedicated to student success.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.150

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.032
GPT teacher head0.374
Teacher spread0.342 · 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 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

Citations25
Published2012
Admission routes3
Has abstractyes

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