Bibliographic record
Abstract
The use of the Delphi technique is prevalent across health sciences research, and it is used to identify priorities, reach consensus on issues of importance and establish clinical guidelines. Thus, as a form of expert opinion research, it can address fundamental questions present in healthcare. However, there is little guidance on how to conduct them, resulting in heterogenous Delphi studies and methodological confusion. Therefore, the purpose of this review is to introduce the use of the Delphi method, assess the application of the Delphi technique within health sciences research, discuss areas of methodological uncertainty and propose recommendations. Advantages of the use of Delphi include anonymity, controlled feedback, flexibility for the choice of statistical analysis, and the ability to gather participants from geographically diverse areas. Areas of methodological uncertainty worthy of further discussion broadly include experts and data management. For experts, the definition and number of participants remain issues of contention, while there are ongoing difficulties with expert selection and retention. For data management, there are issues with data collection, defining consensus and methods of data analysis, such as percent agreement, central tendency, measures of dispersion, and inferential statistics. Overall, the use of Delphi addresses important issues present in health sciences research, but methodological issues remain. It is likely that the aggregation of future Delphi studies will eventually pave the way for more comprehensive reporting guidelines and subsequent methodological clarity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.049 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".