The simulation coaching concept - A step towards expertise
Bibliographic record
Abstract
Background and objective: This paper presents a sub-study of an ongoing research and development project (August 1, 2017-December 31, 2019), whose aim has been to use simulation-based coaching to meet social and healthcare staff’s self-reported learning needs in 20 small and medium-sized enterprises in Finland. Two regional educational institutions are responsible for the management of the project. The study aim was to examine the development of self-rated professional competence and expertise of social and healthcare staff, following a simulation coaching project.Methods: An electronic questionnaire was used to collect information about participants’ self-rated expertise, first in November 2017 and again in May 2019 following the simulation-based coaching intervention. IBM SPSS for Windows 25 was used to analyse the data.Results: The respondents appreciated simulation coaching as an effective way of developing expertise and the continuous learning skills of professionals. In this project, coaching was considered to be especially suitable for theoretical and practical management of acute situations; for keeping up with change in society; for anticipating development needs, and for promoting the attractiveness and competitiveness of the company where they worked.Conclusions: The simulation coaching concept, which involves action-based and concrete ways of developing theoretical and practical competence, is well suited for social and healthcare professionals undertaking continuing education. Using the companies’ own facilities facilitates participation and application of new knowledge and 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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".