Exploring the components of physician volunteer engagement: a qualitative investigation of a national Canadian simulation-based training programme
Bibliographic record
Abstract
OBJECTIVES: Conceptual clarity on physician volunteer engagement is lacking in the medical literature. The aim of this study was to present a conceptual framework to describe the elements which influence physician volunteer engagement and to explore volunteer engagement within a national educational programme. SETTING: The context for this study was the Acute Critical Events Simulation (ACES) programme in Canada, which has successfully evolved into a national educational programme, driven by physician volunteers. From 2010 to 2014, the programme recruited 73 volunteer healthcare professionals who contributed to the creation of educational materials and/or served as instructors. METHOD: A conceptual framework was constructed based on an extensive literature review and expert consultation. Secondary qualitative analysis was undertaken on 15 semistructured interviews conducted from 2012 to 2013 with programme directors and healthcare professionals across Canada. An additional 15 interviews were conducted in 2015 with physician volunteers to achieve thematic saturation. Data were analysed iteratively and inductive coding techniques applied. RESULTS: From the physician volunteer data, 11 themes emerged. The most prominent themes included volunteer recruitment, retention, exchange, recognition, educator network and quasi-volunteerism. Captured within these interrelated themes were the framework elements, including the synergistic effects of emotional, cognitive and reciprocal engagement. Behavioural engagement was driven by these factors along with a cue to action, which led to contributions to the ACES programme. CONCLUSION: This investigation provides a preliminary framework and supportive evidence towards understanding the complex construct of physician volunteer engagement. The need for this research is particularly important in present day, where growing fiscal constraints create challenges for medical education to do more with less.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".