Social Communication Implementable and Applicable Lens: A Framework for Addressing Assessment of Social Communication
Bibliographic record
Abstract
PURPOSE: Keegan, Hoepner, et al. (2023) developed a framework for social, cognitive-communication assessment, which was intended to make social communication assessment efficient, comprehensive, and accessible to practicing clinicians. METHOD: Feedback on the published framework was solicited from expert researchers and clinicians at a variety of conference meetings, including the 2023 International Brain Injury Association Congress, 2023 American Speech-Language-Hearing Association Convention, and 2024 International Cognitive-Communication Disorders Conference. The authors incorporated this feedback to develop an updated, clinically applicable framework of communication entitled Social Communication Implementable and Applicable Lens (SoCIAL). The goal of this update was to provide clinically applicable recommendations that clinicians can feasibly implement in their assessment of social communication. RESULTS: The SoCIAL framework is presented and described with the inclusion of contextual factors. The focus of the framework shifted to the scope of practice of a speech-language pathologist and their role in social communication. Practical, accessible methods and tools that can be applied in the context of this framework are identified and critically appraised. CONCLUSIONS: This framework highlights the intersecting variables (contextual, social, personal, and environmental) that clinicians should consider during the assessment process and when working to support collaborative goal setting, as person-centered intervention is planned. While there is a growing literature base that supports a focus on social communication in context, there remains a disconnect between the literature and clinical application that current researchers and practitioners have an opportunity to address. It is our hope that the SoCIAL framework provides a framework for supporting clinical implementation and moving translational research forward. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.28872272.
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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 | 0.099 | 0.087 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.017 | 0.005 |
| Science and technology studies | 0.009 | 0.047 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.006 | 0.013 |
| 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".