Web-Based Interventions to Improve Mental Health, General Caregiving Outcomes, and General Health for Informal Caregivers of Adults With Chronic Conditions Living in the Community: Rapid Evidence Review
Notice bibliographique
Résumé
BACKGROUND: Most adults with chronic conditions live at home and rely on informal caregivers to provide support. Caregiving can result in negative impacts such as poor mental and physical health. eHealth interventions may offer effective and accessible ways to provide education and support to informal caregivers. However, we know little about the impact of Web-based interventions for informal caregivers of community-dwelling adults with chronic conditions. OBJECTIVE: The purpose of this rapid evidence review was to assess the impact of Web-based interventions on mental health, general caregiving outcomes, and general health for informal caregivers of persons with chronic conditions living in the community. METHODS: A rapid evidence review of the current literature was employed to address the study purpose. EMBASE, MEDLINE, PsychInfo, CINAHL, Cochrane, and Ageline were searched covering all studies published from January 1995 to July 2016. Papers were included if they (1) included a Web-based modality to deliver an intervention; (2) included informal, unpaid adult caregivers of community-living adults with a chronic condition; (3) were either a randomized controlled trial (RCT) or controlled clinical trial (CCT); and (4) reported on any caregiver outcome as a result of use or exposure to the intervention. RESULTS: A total of 20 papers (17 studies) were included in this review. Study findings were mixed with both statistically significant and nonsignificant findings on various caregiver outcomes. Of the 17 included studies, 10 had at least one significant outcome. The most commonly assessed outcome was mental health, which included depressive symptoms, stress or distress, and anxiety. Twelve papers examined the impact of interventions on the outcome of depressive symptoms; 4 found a significant decrease in depressive symptoms. Eight studies examined the outcome of stress or distress; 4 of these found a significant reduction in stress or distress as a result of the intervention. Three studies examined the outcome of anxiety; 2 of these found significant reductions in anxiety. Other significant results of the interventions were seen in the outcomes of caregiver gain (ie, positive aspects of caregiving), knowledge, bonding, reduction of anger-hostility, and negative mood. Based on this review, it is not possible to determine which interventions were most effective since studies differed in their design, sample, and intervention. Study results suggest that Web-based interventions may result in reduced depressive symptoms, anxiety, and stress or distress among informal caregivers of adults with chronic conditions in the community. CONCLUSIONS: This is the first review assessing the impact of Web-based technologies on mental health, general caregiving outcomes, and general health for caregivers of adults with chronic conditions living in the community. Further rigorous research is needed that includes adequately powered studies examining the critical components of the intervention and the dosage needed to have an effect.
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 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 | 0,010 | 0,043 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,005 |
| Bibliométrie | 0,009 | 0,009 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
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 ».