Looking Through the Kaleidoscope: Stakeholder Perspectives on an International Speech-Language Pathology Placement
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.034 | 0.016 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".