Using treatment to improve the production of emotive adjectives in aphasia: a single-case study
Notice bibliographique
Résumé
Background: Emotive adjectives are used in everyday conversations to express opinions and feelings and make evaluations (e.g., “interesting”, “intelligent”). It has been reported that people with aphasia have difficulty using emotive language and that they would like this to be targeted in therapy. However, the literature provides little guidance whether it is possible to improve production of emotive adjectives.Aims: Our aim was to test the hypothesis that a treatment technique that has been found to be effective in improving noun and verb retrieval (Repetition in the Presence of a Picture) would be effective in improving production of emotive adjectives.Methods & Procedures: This study involved GEC, a 66-year-old English-speaking man who presented with non-fluent aphasia including frequent word-finding difficulties and impaired production of emotive adjectives following a left-hemisphere stroke. Treatment was carried out using a single-subject multiple-baseline design consisting of two treatment periods each of 2 weeks preceded by four baseline measurements, with one within-treatment measurement and three post-treatment measurements (immediately, 1-week, and 11 weeks after the end of the treatment programme). The treatment comprised weekly meetings with the therapist and computer-presented, self-paced, home-practice using to treat 72 emotive adjectives (36 positive and 36 negative adjectives) associated with 24 pictures.Outcomes & Results: GEC’s ability to produce treated adjectives for treated pictures significantly improved. The effect was maintained for the positive items with maintenance for negative items close to significant. However, these item-specific effects of treatment did not generalise: No significant improvement was observed in producing new, untreated labels for the treated pictures. Nor was GEC able to use treated labels with pictures other than those with which they were treated. In addition, GEC’s performance in a connected speech task remained unchanged. These results indicate that the treatment effects were not only item-specific but also task-specific.Conclusions: This study provides the first demonstration that emotive adjective retrieval may be improved using a treatment method similar to that commonly used for treating nouns and verbs. This result was achieved after only 2 weeks of practice at home with a computer including sparse meetings with a therapist and targeting only single-word production of adjectives. While there was no evidence of generalisation across items or tasks, this study encourages further exploration of the topic. This should include replication across participants and the inclusion of more natural conversational tasks in the treatment to facilitate transfer.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».