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Looking Through the Kaleidoscope: Stakeholder Perspectives on an International Speech-Language Pathology Placement

2019· article· en· W2990659852 on OpenAlexaffabout
Bea Staley, Lynn Ellwood, David Rochus, Rachael Gibson, Dain Hong, Katie Kwan

Bibliographic record

VenuePerspectives of the ASHA Special Interest Groups · 2019
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGeneral partnershipAgency (philosophy)StakeholderSpeech-Language PathologyWork (physics)ConversationMedical educationKaleidoscopePublic relationsBest practicePsychologyPedagogySociologyPolitical scienceMedicineSocial science

Abstract

fetched live from OpenAlex

Purpose In this article, we consider the literature on international student placements to contextualize and describe a 10-year relationship that enables speech-language pathology (SLP) students in their final year of studies at a Canadian university to complete a 10-week clinical placement with a nongovernmental organization in Kenya. Method This work can be best described as a qualitative case study that includes the varied perspectives of students and colleagues (from both minority and majority worlds) involved in this partnership, which annually places Canadian SLP students in Western Kenya. Perspectives include the director of the nongovernmental organization, 1 East African speech-language pathologist responsible for hosting and supervising students, the clinical placement director in Canada, and the students themselves. The perspectives of minority world universities and their students tend to be privileged and more widely represented. This work contributes to the literature by including the views of the hosting majority world SLP partner agency. Results The varied perspectives reveal that the perceived advantages and difficulties of international SLP clinical placements differ for various stakeholders. Conclusions As the SLP profession moves forward in an increasingly globalized world, it may be necessary for SLP peak professional bodies to develop best practice frameworks for overseas engagement.

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)0.013
metaresearch head score (Gemma)0.014
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0340.016
Scholarly communication0.0120.007
Open science0.0020.016
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0060.001

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 head0.035
GPT teacher head0.329
Teacher spread0.294 · 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

Citations6
Published2019
Admission routes2
Has abstractyes

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