Using the Delphi Technique to Explore Complex Concepts in Speech-Language Pathology: An Illustrative Example From Children's Social Communication
Bibliographic record
Abstract
PURPOSE: In recent years, there has been an increasing interest in expanding the research approaches that speech-language pathologists utilize, particularly for addressing complex questions. Consensus-building techniques can be useful for addressing such questions. The Delphi technique is a consensus-building process involving structured communication among members of an expert panel via independent responses to iterative rounds of questionnaires. The purpose of this research note is to describe and demonstrate the Delphi technique using an application to a complex problem in speech-language pathology, that is, the bases of social communication and pragmatics. METHOD: The Delphi technique was described and illustrated via the following study: 10 expert speech-language pathologists participated in a 3-round Delphi study. Participants were asked to list the key features of social communication and pragmatics in Round 1. Questions for Rounds 2 and 3 were developed on the basis of the participants' responses to previous rounds. RESULTS: The Delphi technique was useful in bringing participants to consensus on the key features of social communication and pragmatics and offered a starting point for the continued exploration of this complex problem. CONCLUSION: A discussion of the benefits and limitations of the technique is included, highlighting the utility of the technique to the field of speech-language pathology.
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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 | 0.035 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| 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".