“ <i>Voices from the ground</i> ”: reverberations from a community of practice
Bibliographic record
Abstract
We thank Kaur et al. [1] for their thoughtful reading of our Delphi study [2]. Our findings have contributed to the development of a tool which is now embedded into the learning architecture [3] of the Vital Anaesthesia Simulation Training (VAST) Community of Practice [4]. This a globally distributed group of healthcare practitioners dedicated to offering high-quality simulation in low-resource settings. Kaur et al. raise questions about equitable partnerships in research. We believe they present a false dichotomy that we now seek to address. As highlighted in the Delphi study reflexivity statement, that work was part of a longitudinal research agenda aligned with the theoretical framework for learning within communities of practice [3]. According to this theory, community members develop skills in specific cultural and social environments through which they innovate, create new knowledge and develop a collective understanding of the practice of their community. The work of our community is situated within a nuanced definition of low-resource settings whereby resource limitations pertain less to the overall economic status of a country and more to the capacity of individuals, departments and organisations; location-specific resource availability; and logistical constraints. Low-resource settings can exist in high-income countries (HICs) (e.g. rural and remote settings). We explored simulation facilitation competencies from this perspective. We echo the belief that focusing on the dichotomy of HICs vs. low- and middle-income countries (LMICs), when not directly relevant, may foster ‘otherness’, perpetuate divisions and continue to extend destructive legacies in global health research [5]. Ambimbola highlights that “The growing concerns about the imbalances in authorship are a tangible proxy for concerns about power asymmetries in the production (and benefits) of knowledge in global health” [6]. Rather than arbitrary and superficial assessment of authorship percentages, Ambimbola states that considerations should be around “who we are as authors, who we imagine we write for (i.e. gaze), and the position or standpoint from which we write (i.e. pose)” [6]. Our modified Delphi study was conducted by members of our community primarily to support reflection and performance improvement within our community. This includes simulation educators who live and work in LMICs; those practicing simulation in HICs (where the context is consistent with our definitions of low-resource settings); and those from both HICs and LMICs who conduct simulation in foreign settings. Beyond oversimplified concerns of contribution, we were surprised by the assertion by Kaur et al. that more personnel from LMICs “may have led to greater understanding of local perceptions and challenges, and ultimately more insightful results and greater engagement”. Our study invited discussion on a topic where there is an extensive knowledge gap [2]. The scoring came from a diverse group of study participants that included simulation learners, novice facilitators and simulation experts from wide-ranging geographical and cultural contexts. There was breadth and depth of experience, with those participants who reported > 10 years of simulation experience having facilitated in 38 countries. In combining diversity with anonymity, we believe that the resulting framework truly represents the voice of our community. The overall 93% retention rate across four Delphi rounds is testament to community members' engagement. Our study used an inward gaze and in applying Ambimbola's authorial reflexivity matrix, the authorship fits the suggested ‘ideal’ approach [6]. We agree with Kaur et al. that equitable research partnerships are crucial. We will continue to apply Ambimbola's matrix in future research by members of our community.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".