Current Practices Among Speech-Language Pathologists for Mild Traumatic Brain Injury: A Mixed-Methods Modified Delphi Approach
Bibliographic record
Abstract
Purpose The study purpose was to capture the clinical practice patterns of speech-language pathologists (SLPs) treating mild traumatic brain injury (mTBI; concussion). Study aims were to (a) characterize expert SLP practice patterns for the management of mTBI and (b) use qualitative content analysis to explore areas of quantitative variability within participants' responses. Method Using a modified Delphi approach, SLPs completed an online survey, with Round 1 responses shaping questions for Round 2. Round 2 results were analyzed using a concurrent partially mixed-methods approach with quantitative and qualitative items. Quantitative consensus agreement levels were set prestudy at 75% agreement or higher. Topic-level items that did not reach consensus were analyzed using qualitative content analysis. Participants SLPs engaging in a specialty mTBI-SLP networking group were invited to participate. Round 1 had nine participants (United States: 4, Canada: 5). Round 2 had 18 participants (United States: 12, Canada: 6), with a mean of 15.7 years of experience in mTBI (range: 3-33) and a mean of 7.6 clients with mTBI seen weekly (range: 1-25). Results Nearly all topic-level practice items met quantitative consensus (42/45). Consensus areas included using conceptual frameworks, interprofessional teaming, assessment and intervention practices, goal setting, and outcomes. Functional, collaborative, and client-centered care anchored clinical practice. Areas lacking consensus included SLP roles in mTBI mental and somatic health symptoms, specific measurement tools, and intervention dosages. Qualitative themes that emerged included limited interprofessional awareness for the role of SLPs in mTBI and challenges in measurement selection. Conclusions Study aims were met with clear consensus on clinical patterns implemented by SLPs specializing in mTBI. Results will inform both current clinical practices and future practice guidelines. High-level guidance and advocacy are needed to clarify SLP practice concerns and advance interprofessional education. Future work should address identified gaps, including targeted assessment tools and consensus on intervention methods.
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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 | 0.111 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".