Adapting Compassion Education Through Technology-Enhanced Learning: An Exploratory Study
Bibliographic record
Abstract
PURPOSE: Compassion is central to health care. Efforts to promote compassion through educational interventions for health professionals show promise, yet such education has not gained widespread dissemination. Adapting compassion education through technology-enhanced learning may provide an opportunity to enhance the scale and spread of compassion education. However, challenges are inherent in translating such curricula for online delivery. In this study, the authors explored how technology influences the delivery of compassion education for health professionals. METHOD: Using constructivist grounded theory methodology, the authors conducted semistructured interviews with 13 participants from across Ontario, Canada, from March to October 2019. The sample consisted of individuals who had experience with the design and evaluation of compassion education for health professionals. The interviews were coded and inductively analyzed to identify pertinent themes using constant comparative analysis. The study originated at the Schulich School of Medicine & Dentistry, Western University, London, Ontario, Canada. RESULTS: Participants provided a range of responses regarding technology and compassion education. While participants revealed concerns about the constraints of technology on human interaction, they also described technology as both inevitable and necessary for the delivery of future compassionate care curricula. Participants also shared ways in which technology may enhance compassion education for health professionals by increasing accessibility and learner comfort with vulnerability. Addressing technological ambivalence, improving facilitation, and maintaining a balance between face-to-face instruction and technology-enhanced learning were identified as elements that could advance compassion education into the future. CONCLUSIONS: Compassion education can be enhanced by technology; however, evidence-informed adaptation may require deliberate efforts to maintain some level of face-to-face interaction to ensure that technology does not erode human connection. Further research is required to address the uncertainties surrounding technology and compassion education as identified by participants. These findings provide educators with guidance for adapting compassionate care curricula into a digital domain.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".