A strategy-based reading intervention in aphasia: a mixed methods approach to explore individual differences in treatment response
Why this work is in the frame
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Bibliographic record
Abstract
Background Persons with aphasia (PWA) frequently experience difficulties in text-level reading comprehension. StraTexT is a strategy-based intervention to treat text comprehension in aphasia. Previous studies on StraTexT have analysed quantitative and qualitative data of an intervention study with 22 participants, providing estimates of the magnitude of treatment effects, insights into subjectively perceived improvements, and a comparison with a control condition. Individual-level quantitative analyses and interview data have not been integrated, and it is unclear for which individuals StraTexT may be particularly beneficial when considering both types of data. The rehabilitation treatment specification system (RTSS) provides a helpful framework to integrate the data.Aims (1) to describe different treatment response profiles to the targets improvement in text comprehension and transfer of reading strategies to everyday life based on self-perceived and performance-based measures, and (2) to explore similarities and differences within and across these response profiles in pre-treatment participant characteristics and patterns of change.Methods We integrated the self-perceived and performance-based outcomes on change in text comprehension, reading abilities and the transfer of reading strategies. Then, we identified different response profiles based on similarities in treatment response. We described participant characteristics for individuals at the extreme ends of the response continuum. Finally, based on the interview data, we described similarities and differences in perceived changes in reading functions, cognitive functions, and transfer within and across different response profiles to better understand applicable mechanisms of action.Outcomes and Results Regarding the selected targets, we identified seven response profiles. Subjective and objective improvement did not correlate. The descriptions of pre-treatment characteristics revealed that reading speed, discrepancies between subjective and objective measures, and several other factors differed in PWA with different response profiles. However, self-perceived change in reading functions, cognitive functions and strategy use was heterogeneous within and across response profiles.Conclusions StraTexT contributes to objective and subjective improvements in text comprehension, even in individuals who do not transfer the strategies to everyday reading. The interview data suggests that for each PWA, a different subset of StraTexT’s mechanisms of action may be at work. This likely reflects differences in underlying impairments. We propose potential indicators for candidacy decisions, including the reading speed and the discrepancy between subjective and objective measures, to be explored in future research, and emphasize the need for both performance-based and self-rated outcomes. Furthermore, our results support the use of multiple- rather than single strategy interventions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it