Evaluating a simulation-based telecare training program for home healthcare professionals: A trainee perspective
Bibliographic record
Abstract
Background: The provision and use of telecare services implies new ways of working for home healthcare staff. To gain the knowledge, skills and attitudes necessary for sound telecare practice, staff are in need of thorough training opportunities. Simulation has been suggested as a useful approach to prepare healthcare professionals for providing telecare services. The aim of this study was to test and evaluate a simulation-based telecare training program for qualified healthcare professionals and explore whether it met intended training objectives from the perspective of the trainees.Methods: A total of 14 healthcare professionals working in home healthcare services participated in up to two training sessions, each across two separate days. Data were collected by way of four tape-recorded focus group interviews and field notes from non-participant observations of eight simulation sessions, and were analysed by way of systematic text condensation.Results: The analysis resulted in seven categories addressing trainees’ experiences of partaking in simulated virtual visits; their perceptions of simulation-based telecare training; and their views on the main learning outcomes from the simulation-based training program in question.Conclusions: Simulation-based training provides trainees with realistic insight into the knowledge and skills required for new ways of working through telecare and can thus be a useful way of preparing healthcare professionals for the delivery of telecare services such as virtual home healthcare visits.
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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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".