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Record W4392748472 · doi:10.1080/02701960.2024.2328520

Enhancing gerontological social work education: Curriculum insights from offering a clinical gerontology certificate

2024· article· en· W4392748472 on OpenAlexaffabout
Jennifer Hewson, Kaylin Epp, Christine A. Walsh, Carolyn Gulbrandsen, Salimah Walji-Shivji

Bibliographic record

VenueGerontology & Geriatrics Education · 2024
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCurriculumCertificateMedical educationSocial workExperiential learningMedicineService-learningNursingPedagogyPsychologyPolitical science

Abstract

fetched live from OpenAlex

With the increasing aging population there is a need for more gerontological social work practitioners; however, such training for social workers in Canada is limited. To help address this gap, one faculty of social work developed a graduate level clinical social work practice certificate with a specialization in gerontology. In this paper we explore students' and instructors' perspectives about the curriculum, delivery, and impact of this certificate, and provide recommendations for improvement, particularly with respect to the clinical nature of the courses. Eight students and four instructors participated in the study. Strengths and opportunities for enhancement were identified for curriculum and delivery. Study findings also indicated that further curriculum development should focus on enhancing clinical skill development and providing more practice experience. Implications arising from these findings included developing clinical skills through experiential learning, interprofessional education, and service learning.

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)0.004
metaresearch head score (Gemma)0.009
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 score0.004
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.001
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 head0.138
GPT teacher head0.485
Teacher spread0.347 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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