Internationally‐educated health professionals: a distance education multiple cultures model
Bibliographic record
Abstract
Purpose This paper aims to explore issues that must be addressed in post‐secondary educational planning and delivery such that social cultural factors within the learning environment are recognized in ways that affirm the learner's cultural traditions. Design/methodology/approach The adoption of a multiple cultures model of instructional design with an emphasis on implementing flexible learning using instructional technology is proposed. Findings The paper finds that as student mobility continues to increase across educational programs and geographic borders, the need to accommodate cultural differences in an increasingly heterogeneous study will have to increase dramatically and, for this to occur, institutions and faculty will have to improve their insight into cultural and learning differences that affect teaching and learning. Practical implications Distance education courses are commonly offered in professional upgrading or “bridging” programs as one solution to addressing the apparent knowledge and experience gaps of newly immigrated internationally‐educated practitioners. Useful strategies for accommodating individual styles and preferences in a multiple cultures professional online learning context have been described. Originality/value Learning preferences and styles are inextricably related to cultural background as well as curricular and course design. This paper provides a much‐needed professional distance education framework that integrates the skills and values of the student with those of the local professional community to create a unified and authentic learning environment.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".