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Record W1651859593 · doi:10.18192/riss-ijhs.v4i1.1222

Health Sciences (HSS) Buddy Program: Evaluation of its First Year

2014· article· en· W1651859593 on OpenAlexaffvenueabout
Mostafa Abdul-Fattah, Rita Hafizi, Hiba Abdul-Fattah, Sonia Gulati, Raywat Deonandan

Bibliographic record

VenueRevue interdisciplinaire des sciences de la santé - Interdisciplinary Journal of Health Sciences · 2014
Typearticle
Languageen
FieldPsychology
TopicMentoring and Academic Development
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCasualMedical educationPsychologyAnxietyAcademic yearMathematics educationMedicinePolitical science

Abstract

fetched live from OpenAlex

In the 2011-2012 academic year, the HSS Buddy Program pilot project was implemented in the Interdisciplinary School of Health Sciences at the University of Ottawa. Intended to address rising student anxiety levels, the program teamed freshmen (first year) students with groups of older students to promote more instances of casual social interaction. Participants’ perceptions of the program were universally positive in terms of how enjoyable it was, its usefulness, and its relevance to student needs. Suggested improvements include recruiting of more male participants, liaising with school administrators to help avoid scheduling conflicts, starting the program earlier in the academic year, and forming social groups with fewer students. Overall, the approach undertaken by the Buddy Program was seen to be a valuable one worthy of continuation and growth.

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)0.009
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.114
GPT teacher head0.503
Teacher spread0.389 · 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

Citations0
Published2014
Admission routes3
Has abstractyes

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