Agile Delphi methodology: A case study on how technology impacts burnout syndrome in the post-pandemic era
Bibliographic record
Abstract
Introduction: In the post-pandemic era, many habits in different areas of our lives have changed. The exponential growth in the use of technology to perform work activities is one of them. At the same time, there has been a marked increase in burnout syndrome. Is this a coincidence? Could they be two interconnected developments? What if they were? Can we use technology to mitigate this syndrome? This article presents the agile Delphi methodology (MAD), an evolved version of the Delphi method, adapted to the needs of modern-day society. Methods: To drive Occupational Health and Safety (OHS) experts to reach a consensus on what technological and non-technological factors could be causing the burnout syndrome experienced by workers in the post-pandemic era, MAD has been used in a specific case study. This study formally presents MAD and describes the stages enacted to run Delphi experiments agilely. Results: MAD is more efficient than the traditional Delphi methodology, reducing the time taken to reach a consensus and increasing the quality of the resulting products. Discussion: OHS experts identified factors that affect and cause an increase in burnout syndrome as well as mechanisms to mitigate their effects. The next step is to evaluate whether, as the experts predict, burnout syndrome decreases with the mechanisms identified in this case study.
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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 | 0.020 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".