Upscaling communication skills training – lessons learned from international initiatives
Bibliographic record
Abstract
OBJECTIVE: To collect experiences and to identify the main facilitators and barriers for the implementation process of large scale communication training programs. METHODS: Using a multiple case study design, data was collected from leaders of the individual programs in Australia, Ireland, Austria and Denmark. The RE-AIM framework was used to evaluate the components: Reach, Effectiveness, Adoption, Implementation, and Maintenance of the programs. RESULTS: The programs, all based on the Calgary-Cambridge Guide, succeeded in reaching the intended target groups corresponding to between 446 and 3000 healthcare workers. New courses are planned and so far the outcome of the intervention has been investigated in two countries. The fact that implementation, including educating trainers, relies on a few individuals was identified as the main challenge. CONCLUSION: Large scale communication training programs based on the Calgary-Cambridge Guide can be implemented and adopted in multiple different healthcare settings across a national health system culture. The importance of standardized trainer education and adaption of the programs to clinical practice was highlighted. PRACTICE IMPLICATIONS: In order to address the sustainability of the programs and to allow the intervention to scale up, it is important to prioritise and allocate resources at the political and organizational level.
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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 | 0.021 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".