SDMPH 10-year Anniversary Conference Modified Delphi Study
Bibliographic record
Abstract
OBJECTIVES: The SDMPH 10-year anniversary conference created an opportunity for a researcher to present at a professional association conference to advance their research by seeking consensus of statements using Delphi methodology. METHODS: Conference attendees and SDMPH members who did not attend the conference were identified as Delphi experts. Experts rated their agreement of each statement on a 7- point linear numeric scale. Consensus amongst experts was defined as a standard deviation < = 1. Presenters submitted statements relevant to advancing their research to the authors to edit to fit Delphi statement formatting.Statements attaining consensus were included in the final report after the first round. Those not attaining consensus moved to the second round in which experts were shown the mean response of the expert panel and their own response for opportunity to reconsider their rating for that round. If reconsideration attained consensus, these statements were included in the final report. This process repeated in a third and final round. RESULTS: 37 Experts agreed to participate in the first round; 35 completed the second round, and 34 completed the third round; 35 statements attained consensus; 3 statements did not attain consensus. CONCLUSIONS: A Delphi technique was used to establish expert consensus of statements submitted by the SDMPH conference presenters to guide their future education, research, and training.
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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 | 0.036 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".