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Record W1854777881 · doi:10.1921/jpts.v13i2-3.820

Enhancing the practice learning experiences of BME students: Strategies for practice education

2015· article· en· W1854777881 on OpenAlexaboutno aff
Prospera Tedam

Bibliographic record

VenueThe Journal of Practice Teaching in Health and Social Work · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsnot available
Fundersnot available
KeywordsPracticumEthnic groupSocial workPedagogySet (abstract data type)Work (physics)Medical educationSociologyPolitical scienceMedicine

Abstract

fetched live from OpenAlex

Practice learning, also known as field education or practicum is central to social work education not only in the UK, but also in countries such as the United States of America, Australia, New Zealand, Canada and South Africa. It presents students with opportunities to integrate academic learning and practice experience with people, their families, communities and environments, and enables the development and enhancement of key skills and social work values. In England, recent studies have highlighted the disparity between black students and white students in terms of progression and attainment on social work qualifying programmes, and practice placements are becoming notorious as sites of difficulty for many students from black and minority ethnic (BME) backgrounds. This paper focuses on a sub-group (black African students), an ethnic minority group with a unique set of characteristics which have been found to contribute to their specific experiences of practice education in the UK.This paper outlines some strategies which have been proposed by black African students as necessary and central to enhancing their practice learning experiences and outcomes on social work programmes. This article was developed from a paper presented to the International Conference on Practice Learning in Scotland (4-7 April 2014).

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)0.057
metaresearch head score (Gemma)0.095
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.801
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0570.095
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.000
Scholarly communication0.0000.003
Open science0.0000.000
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 head0.060
GPT teacher head0.486
Teacher spread0.425 · 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; both teacher heads agree on what is shown here.

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

Citations9
Published2015
Admission routes1
Has abstractyes

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