Educational games in geriatric medicine education: a systematic review
Bibliographic record
Abstract
OBJECTIVE: To systematically review the medical literature to assess the effect of geriatric educational games on the satisfaction, knowledge, beliefs, attitudes and behaviors of health care professionals. METHODS: We conducted a systematic review following the Cochrane Collaboration methodology including an electronic search of 10 electronic databases. We included randomized controlled trials (RCT) and controlled clinical trials (CCT) and excluded single arm studies. Population of interests included members (practitioners or students) of the health care professions. Outcomes of interests were participants' satisfaction, knowledge, beliefs, attitude, and behaviors. RESULTS: We included 8 studies evaluating 5 geriatric role playing games, all conducted in United States. All studies suffered from one or more methodological limitations but the overall quality of evidence was acceptable. None of the studies assessed the effects of the games on beliefs or behaviors. None of the 8 studies reported a statistically significant difference between the 2 groups in terms of change in attitude. One study assessed the impact on knowledge and found non-statistically significant difference between the 2 groups. Two studies found levels of satisfaction among participants to be high. We did not conduct a planned meta-analysis because the included studies either reported no statistical data or reported different summary statistics. CONCLUSION: The available evidence does not support the use of role playing interventions in geriatric medical education with the aim of improving the attitudes towards the elderly.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".