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Record W3217702809 · doi:10.7939/r3-bz9a-eg87

Serious Games to Train Neonatal Resuscitation

2021· article· en· W3217702809 on OpenAlexaboutno aff
Simran K. Ghoman

Bibliographic record

VenueUniversity of Alberta Library · 2021
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsResuscitationMedicineNeonatal resuscitationIntensive care medicineComputer scienceMedical emergencyEmergency medicine

Abstract

fetched live from OpenAlex

Each year, 10-20% of newborns (about 13-26 million babies) worldwide will need help to breathe at birth. Helping babies breathe is the cornerstone of neonatal resuscitation. Healthcare providers (HCPs) must perform many tasks quickly and correctly to help babies breathe and prevent irreversible organ injury or death. However even with their help, one million of these babies will die. Medical errors by HCPs remain common and are responsible for 60% of this mortality. To address this staggering gap, guidelines recommend frequent simulation training. Simulation training prepares HCPs to deliver high-quality care while maintaining patient safety. Therefore, to be certified as a neonatal resuscitation provider, HCPs must complete the simulation-based Neonatal Resuscitation Program course once every two years. However, HCPs’ knowledge and skills decrease significantly over time, as early as three months after training. More frequent training is needed but is often prohibitively resource intensive, requiring significant financial and personnel investment. Traditional simulation requires learners to coordinate time away from the clinic to practice under the supervision of a trained instructor and operations specialist, in a lab outfitted with expensive manikins and equipment to mimic the delivery room. Therefore, most HCPs are unable to access adequate training to safely provide care for their newborn patients. Simulation-based serious games may offer a solution to improve access to training. Serious games use elements like competition and emotional design to teach knowledge or skills. This thesis examines the simulation-based serious game RETAIN (REsuscitation TrAINing for healthcare professionals, Retain Labs Medical Inc., Edmonton, Canada) which fits this description. In the RETAIN board game and digital game, learners undergo neonatal resuscitation scenarios to practice their knowledge, communication, and decision-making skills while stabilizing a simulated newborn in distress. The main goal of this research project was to investigate the educational outcomes of using the RETAIN simulators for training and assessment of experienced neonatal resuscitation providers from a level-three perinatal center in Edmonton, Canada. I hypothesized that i) the board game could be used as a summative assessment of HCPs’ knowledge, and ii) training with the digital game would improve HCPs’ short- and long-term knowledge retention, maintenance, and transfer. In the first study to assess summative assessment with the board game, I measured HCPs’ performance on an open-answer written test, compared to their performance on the board game. In the second study to assess longitudinal knowledge with the digital game, I measured participants’ incoming knowledge with a pre-test simulation scenario. Next, participants underwent two training scenarios with the digital simulator to practice their neonatal resuscitation knowledge. I then measured participants’ knowledge improvement by administering a post-test immediately after training, long-term knowledge retention by repeating the post-test 2 months after training, and knowledge transfer by administering a novel assessment scenario and medium, 5 months after training. Across the two studies, I quantitatively and qualitatively measured participants’ mindset, habits, and attitudes towards RETAIN, technology, board games, and other educational media with post-session surveys. The results showed that participants performed better on the board game than on the open-answer test, especially if they reported having more experience with board games overall. The board game also allowed for deeper probing of HCPs’ knowledge, like explaining ventilation corrective steps, compared to the written test. In the second study, I observed a significant improvement in participants’ performance immediately after training, which was maintained 2 months later. I also observed successful knowledge transfer, with participants’ best performance demonstrated on this assessment. Overall, participants reported positive attitudes towards RETAIN, technology, and growth mindset. Negative feedback was clustered around usability issues with the digital game. I concluded that the RETAIN simulators supported successful educational outcomes for experienced neonatal resuscitation providers. The board game functioned as an enjoyable and clinically relevant summative assessment, and the digital game facilitated knowledge improvement, retention, and transfer over time. HCPs also expressed positive attitudes towards the simulators, indicating their receptiveness towards these media for continuing healthcare education. Simulation-based serious games are well-positioned to address challenges of traditional simulation training, including improving access to training for urban and rural providers; or facilitating distanced learning during the current pandemic and beyond. Further research is needed to understand how training may ultimately lead to better care and improve health outcomes for our smallest patients.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0200.005

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.

Opus teacher head0.013
GPT teacher head0.248
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

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Citations0
Published2021
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