THE IMPACT OF MINDFULNESS MEDITATION PRACTICE ON REDUCING ANXIETY AND IMPROVING LINGUISTIC MEASURES FOR POST-STROKE PATIENTS LIVING WITH APHASIA
Bibliographic record
Abstract
Approximately 30- 35% of adult patients admitted to hospital with a diagnosis of stroke have symptoms of Aphasia at discharge (1). Individuals living with Aphasia describe feeling u201canxiousu201d when communicating by using language, and research has proposed that this may be due to an emotional state of u201clinguistic anxietyu201d.(2) The emotional impact of Aphasia has been shown to have a marked negative impact on recovery, response to rehabilitation, as well as psychosocial adjustment. (3) While Mindfulness Meditation is being increasingly and effectively incorporated into physical rehabilitation programs (4), its effectiveness for individuals with Aphasia has had limited research to date. This poster explores the role of Mindfulness Meditation for adults living with Aphasia, as a useful and complementary technique to incorporate into SLP therapy. At this time, Mindfulness-Based Stress Reduction (MBSR) has been used with ten patients in this clinicianu2019s Outpatient practice. The results have been very positive. Patientsu2019 self-rating for anxiety, pre- and post- speech samples, as well as linguistic measures will be shared.1. Dickey L, Kagan A, Lindsay MP, Fang J, Rowland A. Incidence and profile of inpatient stroke-induced aphasia in Ontario, Canada. Arch Phys Med Rehabil. 2010 Feb; 91(2):196-202.2. Cahana-Amitay D, Albert ML, Pyun S-B, et al. Language as a Stressor in Aphasia. Aphasiology. 2011;25(2):593-614. 3. Code C, Herrmann M. The relevance of emotional and psychosocial factors in aphasia to rehabilitation. Neuropsychological Rehabilitation. 2003;13(1/2):109u2013132. 4. Hardison ME, Roll SC. Mindfulness Interventions in Physical Rehabilitation: A Scoping Review. The American Journal of Occupational Therapy. 2016;70(3)
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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 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".