Audiologic care for clients with multiple medical comorbidities: Modifications to clinical practice and inclusion of family members and caregivers
Notice bibliographique
Résumé
Older adult clients seeking audiologic rehabilitation commonly present with concomitant health issues, such as visual impairment, decline in manual dexterity, and changes in cognitive status. Audiologists may modify assessment and treatment plans to accommodate these health issues, and may include family members and caregivers in their rehab planning as part of a family-centered approach to client care. Charts from 159 clients of a geriatric audiology clinic who presented for a hearing aid evaluation in 2015 were examined to determine a) how frequently audiologists indicated that their clients experienced medical comorbidities related to vision, manual dexterity, and cognition; b) specific modifications to clinical practice made by the audiologists to accommodate for these comorbidities; and c) level of involvement of family members/caregivers in the client’s audiologic rehabilitation, as noted in the chart by the audiologist. The institutional medical health records were also examined to extract any additional information about the clients' health status. The audiologists noted prevalence rates for visual, manual dexterity, and cognitive issues of approximately 50%, 35%, and 45%, respectively. Modifications to clinical practice, such as using rechargeable hearing aids for clients with poor manual dexterity, were made to accommodate these comorbidities. Although the clients were frequently accompanied by a family member or caregiver, in fewer than 20% of the cases did the audiologist explicitly note this information in the chart. Accumulating evidence suggests that the negative consequences of hearing loss, such as social isolation and reduced overall well-being, are not limited to the impaired individual. Rather, hearing loss, especially in older adults, can also have devastating effects on family members and caregivers. We will discuss potential changes to audiologic best practice that could serve to a) accommodate medical comorbidities in older clients, and b) encourage inclusion of significant others to optimize treatment success.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».