Simulation training for improving the quality of care for older people: an independent evaluation of an innovative programme for inter-professional education
Bibliographic record
Abstract
INTRODUCTION: This paper describes the evaluation of a 2-day simulation training programme for staff designed to improve teamwork and inpatient care and compassion in an older persons' unit. OBJECTIVE: The programme was designed to improve inpatient care for older people by using mixed modality simulation exercises to enhance teamwork and empathetic and compassionate care. METHODS: Healthcare professionals took part in: (a) a 1-day human patient simulation course with six scenarios and (b) a 1-day ward-based simulation course involving five 1-h exercises with integrated debriefing. A mixed methods evaluation included observations of the programme, precourse and postcourse confidence rating scales and follow-up interviews with staff at 7-9 weeks post-training. RESULTS: Observations showed enjoyment of the course but some anxiety and apprehension about the simulation environment. Staff self-confidence improved after human patient simulation (t=9; df=56; p<0.001) and ward-based exercises (t=9.3; df=76; p<0.001). Thematic analysis of interview data showed learning in teamwork and patient care. Participants thought that simulation had been beneficial for team practices such as calling for help and verbalising concerns and for improved interaction with patients. Areas to address in future include widening participation across multi-disciplinary teams, enhancing post-training support and exploring further which aspects of the programme enhance compassion and care of older persons. CONCLUSIONS: The study demonstrated that simulation is an effective method for encouraging dignified care and compassion for older persons by teaching team skills and empathetic and sensitive communication with patients and relatives.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".