Advanced medical communications: support for international residents
Bibliographic record
Abstract
Context and settingDespite the acknowledged importance of communication skills in patient care and education, internationally sponsored residents (ISRs) and international medical graduates (IMGs) arriving in Canada from non-English-speaking countries frequently enter residency without any English communications training within the context of their host culture. Although many have learned English medical vocabulary and developed strong academic English skills through their medical studies, few have had significant communicative English experience in the field of medicine. They have also had little or no exposure to the cultural norms of their new home, and the ‘culture’ of the medical profession within it. Why the idea was necessaryCoping with cultural expectations and language differences in North America has been an ongoing challenge for this group of residents. However, there is a paucity of literature indicating how these needs can be met in a way that is acceptable to the residents, who are often very self-conscious when singled out for special training. The purpose of this pilot study was to assess the feasibility of a programme designed specifically to build, in context, the English-language communication skills of this group. What was doneApproximately 4 months into their residency, new ISRs in the Department of Medicine at a Canadian medical school were required to participate in a pilot ‘English for Medical Purposes’ programme. It was also offered on a voluntary basis to 2 Department of Family Medicine IMGs. The primary instructor for the programme was an English-for-specific-purposes (ESP) specialist. A doctor member of the development team also attended each class to offer medical expertise and to help facilitate scenario-based activities. The 18-hour programme, which took place over 6 academic half-days, focused primarily on doctor−patient and doctor–colleague interactions. Participation was encouraged through clinical standardised patient scenarios and case presentation practice. A strong language and culture focus was built into the programme to meet the unique needs of this resident group. Evaluation of results and impact All 5 of the ISRs and 1 of the IMGs completed an anonymous post-programme feedback form consisting of 7 open and 24 7-point Likert scale questions. Although the 5 ISRs had initially been very reluctant to participate, all the participants indicated that the programme should be offered to all incoming international residents. Despite a low rating of pre-participation interest (3.3, standard deviation [SD] 2.3), the mean rating of the value of the programme was high (6.0, SD = 0.9). In addition, the participants' self-evaluation of their communication skills showed significant improvement: self-assessed mean pre-course skills were rated at 3.8 (SD = 0.4) and post-course skills at 6.2 (SD = 0.8) (P = 0.03, 2-tailed t-test). Written comments were all very positive and included a few requests for the programme content to be expanded. Several participants even expressed interest in repeat participation in the programme. The results of this highly successful pilot show that a targeted communications skills programme can be implemented and can achieve high levels of acceptance by international residents. Next steps will involve further programmatic improvements, as well as using harder end-points to assess improvements in communication skills.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".