Development of CliniPup, a Serious Game Aimed at Reducing Perioperative Anxiety and Pain in Children: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: An increasing number of children undergo ambulatory surgery each year, and a significant proportion experience substantial preoperative anxiety and postoperative pain. The management of perioperative anxiety and pain remains challenging in children and is inadequate, which negatively impacts the physical, psychosocial, and economic outcomes. Existing nonpharmacological interventions are costly, time consuming, vary in availability, and lack benefits. Therefore, there is a need for an evidence-based, accessible, nonpharmacological intervention as an adjunct to existing pharmacological alternatives to reduce perioperative anxiety and pain in children undergoing ambulatory surgery. Technology-enabled interventions have been proposed as a method to address the unmet need in this setting. In particular, serious games hold a unique potential to change health beliefs and behaviors in children. OBJECTIVE: The objective of this research was to describe the rationale, scientific evidence, design aspects, and features of CliniPup, a serious game aimed at reducing perioperative anxiety and pain in children undergoing ambulatory surgery. METHODS: The SERES Framework for serious game development was used to create the serious game, CliniPup. In particular, we used a mixed methods approach that consisted of a structured literature review supplemented with ethnographic research, such as expert interviews and a time-motion exercise. The resulting scientific evidence base was leveraged to ensure that the resulting serious game was relevant, realistic, and theory driven. A participatory design approach was applied, wherein clinical experts qualitatively reviewed several versions of the serious game, and an iterative creative process was used to integrate the applicable feedback. RESULTS: CliniPup, a serious game, was developed to incorporate a scientific evidence base from a structured literature review, realistic content collected during ethnographic research such as expert interviews, explicit pedagogical objectives from scientific literature, and game mechanics and user interface design that address key aspects of the evidence. CONCLUSIONS: This report details the systematic development of CliniPup, a serious game aimed at reducing perioperative anxiety and pain in children undergoing ambulatory surgery. Clinical experts validated CliniPup's underlying scientific evidence base and design foundations, suggesting that it was well designed for preliminary evaluation in the target population. An evaluation plan is proposed and briefly described.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".