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Record W4389451537 · doi:10.1080/08841233.2023.2288268

Undergrad and Over 30: Perceptions of Mature Students in a Canadian Bachelor of Social Work Program

2023· article· en· W4389451537 on OpenAlexaffabout
Lea Tufford, Vivian T. Thieu, Rose Zhao, Angélique Jenney

Bibliographic record

VenueJournal of Teaching in Social Work · 2023
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsAlberta Children's HospitalUniversity of CalgaryUniversity of British ColumbiaLaurentian University
Fundersnot available
KeywordsBachelorThematic analysisSocial workCurriculumPsychologyPerceptionMedical educationPedagogyWork (physics)Qualitative researchSociologySocial scienceMedicinePolitical science

Abstract

fetched live from OpenAlex

This study sought to respond to the increased number of mature students in social work education and the understudied topic of their experiences. The research deployed semi-structured interviews and thematic analysis to explore the benefits, challenges, and needs of mature students (N = 19) over the age of 30 in a Canadian Bachelor of Social Work program. Participant responses revealed multiple, intersecting factors that impacted their experiences. Pertinent themes included the decision to return to school, enhanced engagement, and motivation in the pursuit of their degrees as well as their personal challenges, strategies, and perceptions of ageism within the classroom and curriculum. The study found that mature students would benefit from the development of institutional resources specific to their needs including flexible scheduling, assistance with technology, and clearer guidelines for group activities.

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)0.002
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.400
Threshold uncertainty score0.805

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.003
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.036
GPT teacher head0.438
Teacher spread0.402 · 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
Published2023
Admission routes2
Has abstractyes

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