Notice bibliographique
Résumé
Millions of cancer patients and survivors all around the globe suffer from cancer-related fatigue and experience a reduced quality of life due to their cancer and cancer treatment. With our large-scale international waiting-list RCT, including participants from four English-speaking countries (i.e., Australia, Canada, the United Kingdom, and the United States), we demonstrated that fatigue could be reduced and QoL improved by means of a self-management mHealth app (chapter 5). From March till October 2018, we recruited via 76 Facebook Ads and included 755 participants, of which 355 completed the follow-up measure 12 weeks later, at the expense of €22.42 and €47.69 per participant, respectively (chapter 4). We saw that the most interested participants were female, middle-aged, and came from the UK. We think that reaching participants for international mHealth studies via Facebook Ads has potential but can be very costly, especially when more balanced sub-samples are desired. However, we believe that constant optimization and testing of ads can make an essential difference in reducing recruitment costs. Regarding the app’s effectiveness, we learned that participants do not need to engage excessively with the intervention since medium app use (3-8 days) was already significantly associated with fatigue reduction (chapter 5). Our findings on fatigue reduction were statistically significant and clinically relevant since more people recovered in the intervention group than the control group. We explored whether the effect of the intervention was related to specific age groups and saw that the intervention effect was significant across all age groups but even more pronounced in younger individuals (<56 years). Individuals with different education levels and both cancer patients and survivors seemed to benefit significantly from the app. We do not have enough data to compare outcomes between gender, cancer types, and treatment types and must acknowledge that our study sample is limited in its representativeness to Facebook users. We also explored several processes targeted by the app and their effect on fatigue reduction (chapter 6). We found that app access was significantly associated with reduced fatigue severity and interference via the mechanism of reduced fatigue catastrophizing, depression, sleep disruption, and increased mindfulness and physical activity. Besides, we described the experiences we had with applying for ethical approval in different countries (chapter 3). We believe that research guidelines could support scientists aiming to conduct international internet-based studies regarding whether these should be considered single or multi-center trials. We describe where researchers can apply for ethical approval across different countries.
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 | 0,002 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 0,002 |
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 ».