Using an e-Delphi technique in achieving consensus across disciplines for developing best practice in day surgery in Ireland
Bibliographic record
Abstract
Background: The benefits of day surgery are supported internationally by the provision of standards. However, standards from one health jurisdiction are not readily transferable to others as national health strategy, policy and funding are influencing factors. Objective: To determine, through consensus from experts in day surgery, a list of best practice statements for day surgery in Ireland. Methods: A three round e-Delphi technique. Professionals in surgery, anaesthesia, nursing and management involved in day surgery across all hospitals in Ireland were invited to participate as the expert panel. In round 1 a list of proposals for best practice were obtained from panel members. In round 2 experts were asked to rank each statement according to their importance on a nine point scale (1 = not important, 9 = high importance) using an online questionnaire. Consensus was set at 70%, meaning the items that 70% of people deemed to be important were carried over to round 3. A repeat online questionnaire was conducted with the remaining statements in round 3. Results: Round 1 provided 261 statements. These were grouped and reduced to 62 statements for ranking. Following the iterative process over the subsequent two rounds a final list of 40 statements were developed and grouped into six thematic areas. Conclusion: By using an e-Delphi process of gaining consensus among experts working in day surgical services, a list of best practice statements were developed.
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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 | 0.279 | 0.225 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".