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Record W7073916463

Social Work Curriculum Review Case Study: Service Users Tell Us What Makes Effective Social Workers

2016· article· en· W7073916463 on OpenAlexaboutno aff

Bibliographic record

VenueScholars Commons (Wilfrid Laurier University) · 2016
Typearticle
Languageen
FieldEngineering
TopicPhotonic Crystal and Fiber Optics
Canadian institutionsnot available
Fundersnot available
KeywordsSocial workCurriculumAccreditationFocus groupService (business)Service-learningService providerWork (physics)
DOInot available

Abstract

fetched live from OpenAlex

This paper presents the findings from community focus groups, comprised of social service users, and explores the characteristics of effective social workers. Focus groups were conducted as part of a case study to inform a Master of Social Work (MSW) curriculum review at Wilfrid Laurier University’s Faculty of Social Work. Wilfrid Laurier University has two MSW programs—the MSW Aboriginal Field of Study (AFS) and a non-Aboriginal program. The case for this study was the non-Aboriginal MSW program. Ongoing program evaluation that includes feedback from service users honours the knowledge of marginalized communities, and is an accreditation requirement of the Canadian Association for Social Work Education (CASWE). Four focus groups were conducted with a total of 24 individuals who access programs from human service organizations that provide supportive housing, immigrant, or refugee services in the Kitchener-Waterloo area. Service users identified numerous characteristics of effective social workers, including kindness, cultural awareness, and strong communication skills, as well as the need to articulate and address issues of professional suitability. We conclude by querying whether the typical assessment of MSW students’ suitability for the profession is adequate, and provide the AFS wholistic and comprehensive evaluation as an example of an alternative approach to MSW student assessment.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.836
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.222
Teacher spread0.210 · 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.

Study designNot applicable
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
Published2016
Admission routes1
Has abstractyes

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