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Record W3211496550

University Counselling and Psychological Services:Exploring Current Approaches and Student Perceptions

2019· dissertation· en· W3211496550 on OpenAlexaboutno aff
Abigail R. Peyton

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPsychology
TopicCounseling Practices and Supervision
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Mental healthPerceptionService (business)PsychologyMedical educationTriageSample (material)Type of serviceMedicinePsychiatryBusinessMarketing
DOInot available

Abstract

fetched live from OpenAlex

Across Canada, university counselling and psychological services have experienced an increase in both student demand for assistance and complexity of presenting issues. The literature suggests that many universities are struggling to meet the needs of their students and are exploring various systems and methods (e.g., triage systems, online services) to address the need. Further, students’ perceptions of the adequacy and availability of these services are not well represented in the literature. The present study: 1) explored the on-campus counselling and psychological services offered at 55 Canadian universities through analysis of their online information; 2) investigated students’ perceptions of counselling and psychological services at Grenfell Campus, MUN, through a sample of 204 participants (159 women, 20 men, and 25 unspecified) with a mean age of 20.79 years (range: 18-42). The results indicated that while the majority of participants were aware of Grenfell’s counselling and psychological services (78.9%), a much smaller percentage (19.6%) used the services. Student perceptions were analyzed across a wide range of domains, including type of service, expectations of service options, barriers to accessing the service, and the adequacy and efficacy of additional mental health resources, in relation to the Canadian university context.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.769
Threshold uncertainty score0.984

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.178
GPT teacher head0.378
Teacher spread0.200 · 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 designOther design
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
Published2019
Admission routes1
Has abstractyes

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