Using a Delphi Method Approach to Select Theoretical Underpinnings of Crowdsourcing and Rank Their Application to a Crowdsourcing App
Bibliographic record
Abstract
INTRODUCTION: Since the catapult of online learning during the COVID-19 pandemic, most simulation laboratories are now completed virtually, leaving a gap in skills training and potential for technical skills decay. Acquiring standard, commercially available simulators is prohibitively expensive, but three-dimensional (3D) printing may provide an alternative. This project aimed to develop the theoretical foundations of a crowdsourcing Web-based application (Web app) to fill the gap in health professions simulation training equipment via community-based 3D printing. We aimed to discover how to effectively leverage crowdsourcing with local 3D printers and use these resources to produce simulators via this Web app accessed through computers or smart devices. METHODS: First, a scoping literature review was conducted to discover the theoretical underpinnings of crowdsourcing. Second, these review results were ranked by consumer (health field) and producer (3D printing field) groups via modified Delphi method surveys to determine suitable community engagement strategies for the Web app. Third, the results informed different app iteration ideas and were then generalized beyond the app to address scenarios entailing environmental changes and demands. RESULTS: A scoping review revealed 8 crowdsourcing-related theories. Three were deemed most suitable for our context by both participant groups: Motivation Crowding Theory, Social Exchange Theory, and Transaction Cost Theory. Each theory proposed a different crowdsourcing solution that can streamline additive manufacturing within simulation while applicable to multiple contexts. CONCLUSIONS: Results will be aggregated to develop this flexible Web app that adapts to stakeholder needs and ultimately solves this gap by delivering home-based simulation via community mobilization.
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 | 0.124 | 0.152 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.019 | 0.010 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".