RETAIN: A Board Game That Improves Neonatal Resuscitation Knowledge Retention
Bibliographic record
Abstract
Background More than two thirds of neonatal deaths are attributed to human errors during resuscitation. Therefore, the current resuscitation guidelines recommend frequent simulation based medical education (SBME). However, the current SBME approach is expensive, time-intensive, and requires a specialized lab and trained instructors. Hence, it is not offered routinely at all hospitals. We designed the board game “RETAIN” to train healthcare providers (HCPs) in neonatal resuscitation in a cost-friendly and accessible way. Objectives To examine if a board game-based training simulator improves knowledge retention in HCPs. Methods “RETAIN” consists of a board using an image of a baby, visual objects, adjustable timer, monitors, and action cards. Neonatal HCPs at the Royal Alexandra Hospital were invited to participate. Participants completed a written pre-test (resuscitation of a 24-week infant), then played the board game (starting with a tutorial followed by free playing of three evidence-based neonatal resuscitation scenarios). Afterwards, a post-test with the same resuscitation scenario and an opinion survey was completed. The answers from the pre- and post-test were compared to assess HCPs’ knowledge retention. Results Thirty HCPs (four doctors, 12 nurses, and 14 respiratory therapist) participated in the study. Overall, we observed a 10% increase in knowledge retention between the pre- and post-test (49% to 59%, respectively). Temperature management showed the most knowledge gain between the pre- and post-test (14% to 46%, respectively). Placement of a hat (10% to 43%), plastic wrap (27% to 67%), and temperature probe (7% to 30%) improved between the pre- and post-test. Conclusion Knowledge retention increased by 12% between pre- and post-test (49% to 61%, respectively). The improvement in performance and knowledge supports the use of board game simulations for clinical training.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".