Counting what counts: A framework for capturing real‐life outcomes of aphasia intervention
Bibliographic record
Abstract
Background: The initial motivation was our inability to capture the important but often elusive outcomes of interventions that focus on making a difference to the everyday experience of individuals with aphasia and their families. In addition, a review of the literature and input from stakeholder focus groups revealed the lack of an integrated approach to outcome evaluation across diverse approaches to aphasia intervention. Input from focus groups also indicated that existing classifications and models offering potential solutions are not always easily accessible and user friendly. Aims: We aimed to create a user‐friendly conceptual framework for outcome measurement in aphasia that included a focus on real‐life outcomes of intervention and could be easily accessed by clinicians, researchers, policy makers, funders, and those living with aphasia. We wanted to build on existing work, e.g., that of the World Health Organisation, simplify presentation for accessibility, and make specific adaptations relevant to aphasia. By providing a common context for a broad range of outcome tools or measures, we hoped to enable more efficient and effective communication between and among all stakeholders. Main contribution: Living with Aphasia: Framework for Outcome Measurement (A‐FROM) is a conceptual guide to outcome assessment in aphasia that is situated within current thinking about health and disability. This simple platform can be used to frame and broaden thinking concerning outcome measurement for aphasia clinicians and researchers while enhancing the potential for meaningful communication between the clinical community, policy makers, and funders. By integrating Quality of Life and including domains related to environment, participation, and personal identity in the same framework as impairment, the importance of outcomes in all these areas is acknowledged for aphasia in particular and disability in general. A‐FROM has the potential to be used as an advocacy tool. Conclusions: This article is the first presentation of A‐FROM as an alternate guide to outcome measurement in aphasia. Initial ideas regarding applications are discussed. Further development and applications await input from our community of practice.
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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 | 0.122 | 0.181 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| 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".