A critical approach to the development of blended medical communication training materials
Bibliographic record
Abstract
Effective communication between doctors and patients improves patient health outcomes (Hewett et al. 2009; Stewart 1995; Watson et al. 2012), and communication skills courses have become an integrated component of the medical curriculum. However, language-discordant contexts are not fully provided for yet, since “[l]anguage support guides (such as the Calgary-Cambridge guides; Silverman et al. 2005) generally target native speakers rather than language learners, and typically mention the use of a second or foreign language as just one of many issues in cross-cultural communication without providing any solutions or prescriptions to address ensuing communication problems” (Gasiorek and Van de Poel 2012:372). In a South African professional healthcare context, communication challenges are manifold because of the number of languages and cultures involved and because of the diverse views on the nature of a medical encounter. South African public healthcare is further hampered by a lack of doctors (KZN Department of Health 2013). Given the need for tailored communication materials, a new vocational training course in Afrikaans as a second language was introduced for first-year medical students at one South African university applying a blended learning approach (i.e. restricted contact teaching and autonomous online learning). To facilitate the development and implementation process, the European project Medics on the Move (MoM) was adapted and adopted as a communication tool. This paper addresses principles and practices underlying the MoM materials as part of the process of adapting the vocational training concept to the specific South African context, needs and learners. The development process is presented as an integral part of a comprehensive needs analysis (Brown 1995), at the end of which new questions and needs are identified. The results corroborate that MoM-SA is a dynamic and effective teaching/learning tool which functions as a language support system that helps build skills for a multilingual professional 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 | 0.062 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".