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Record W4212951052 · doi:10.1080/02701960.2022.2033975

Building a national network of student leaders in gerontology: Reflections on the student representative program

2022· article· en· W4212951052 on OpenAlexaffabout
Christine Sheppard, Ariane S. Massie, Laura Kadowaki, Kim Thériault, Andrea Rochon, Tia Rogers-Jarrell

Bibliographic record

VenueGerontology & Geriatrics Education · 2022
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsUniversity of OttawaSimon Fraser UniversityYork UniversitySunnybrook Health Science Centre
Fundersnot available
KeywordsMedical educationGerontologyPsychologySociologyMedicine

Abstract

fetched live from OpenAlex

The Canadian Association on Gerontology's Student Connection facilitates a national Student Representative program to promote the field of gerontology at local post-secondary institutions. Student Representatives are expected to host professional development and networking events on their campus to bring together students interested in the field of aging. Student-run groups help foster interest in aging-related careers and research, yet few studies explore how these groups are developed and sustained. As part of this quality improvement project, we examined (1) who participates as a Student Representative; (2) why students choose to participate in the program; and (3) how Student Representatives fulfil their role (including barriers and facilitators). We conclude with a discussion of the challenges that the Student Connection's executive committee has faced supporting this national network and identify opportunities to further enhance the program. Practical implications to support student engagement and promote sustainability of student-driven aginginterest groups are outlined.

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)0.045
metaresearch head score (Gemma)0.036
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.045
Threshold uncertainty score0.241

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0450.036
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0290.006
Scholarly communication0.0090.007
Open science0.0050.020
Research integrity0.0070.011
Insufficient payload (model declined to judge)0.0090.001

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 head0.212
GPT teacher head0.537
Teacher spread0.324 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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