The Effect of a Complementary Therapy Education Seminar on Support Persons of Individuals with Cancer
Notice bibliographique
Résumé
Objectives: Complementary therapy (CT) use is prevalent among individuals living with cancer, who often consult family and friends (i.e., support persons) in making decisions about CT. This study examines the effect of an education seminar for adult cancer patients and support persons on the support persons' use, knowledge, and decision-making processes related to CT. Design: A patient education seminar that included support persons was developed and evaluated as part of a CT decision support research program. Survey data were collected before and after the education seminar to examine its impact on support persons' knowledge and use of CT, as well as their engagement in the CT decision-making process. Setting: The study was conducted in Western Canada. Subjects: 62 adult support persons. Interventions: Participants attended a 4-h CT education seminar at one in four provincial cancer centers. The seminar provided recommendations regarding how to make informed decisions about CT, where to find credible information, and key issues to consider to avoid potential risks of CT use. The evidence related to popular CT was also reviewed. Outcome Measures: The primary outcome was support persons' CT knowledge. Secondary outcomes included CT use, information-seeking behavior, decision self-efficacy, decision conflict, and distress. Results: A significant increase in support persons' CT knowledge was observed, as well as improved confidence in CT decision making. There was no significant difference in participants' CT use following the education seminar. Most indicated they would continue to locate information about CT using the Internet. A significant decrease in support persons' decisional conflict was reported; however, there were no significant change in distress related to CT decision making. Conclusions: This study demonstrates the importance of including support persons in patient education related to CT and the positive impact on their knowledge and treatment decision-making processes. No significant change in CT use, information seeking behavior and distress related to CT decisions, however, was observed in the study.
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| 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) | 0,001 | 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 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 ».