Bibliographic record
Abstract
Clinical borderlands manifest themselves through encounters between people deemed to be in need of health care and health care providers (Mattingly, 2010). This article addresses the problem of inherent asymmetry in the clinical discourse between clinical providers, such as speech–language pathologists (SLPs), and persons with aphasia. Speech–language pathologists, communicating as experts, tend to dominate the discourse regarding the course of treatment, particularly with clients with aphasia who may lack the necessary communicative skills to participate in decision making. Such patterns of communication were apparent in a study reported here that involved thematic analysis of the views of 12 SLPs regarding involving people with aphasia in shared decision making and in analysis of 33 video recordings of these 12 SLPs and 28 people with aphasia during clinical interactions. Although the SLPs stated that they wanted to involve their clients in decision making and took steps to do so, the discourse sample analysis revealed that the SLPs controlled the interaction through their initiations, topic selection, and presentation of limited choices. Alternatives for supporting greater decision-making participation among people with aphasia with their clinicians are discussed.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".