You need to see the world in order to measure it: The importance of a high follow‐up rate
Notice bibliographique
Résumé
In their letter 1, Bedendo & Noto question the use of incentives in clinical trials and argue that their use does not allow one to study the effectiveness of internet-based brief interventions. They call for studies to sacrifice low attrition and not provide incentives to assess the effectiveness of interventions in real-life scenarios. We think this approach is prone to a major risk of bias. High attrition rates threaten study validity and may artificially inflate intervention effects 2. The simple fact of observing individuals in a research study is known to introduce possible bias (the ‘Hawthorne effect’). As such, no research study can truly assess what would happen to subjects outside the research context. A high follow-up rate was a priority for our study, and therefore we offered incentives to participants 3. As stated by Bedendo & Noto, providing incentives to increase follow-up rates in clinical trials may attract participants more interested in the incentive than in the research, and may lead to participants rushing through the study questionnaires or the intervention's content. Even if we assume this is the case, the results would most probably be biased towards the null. In addition, in our study, participants did not receive money but a coupon to download music online (the equivalent of one pop album) at the end of the follow-up period, which is less likely to attract participants willing to participate ‘only for money’. As such, the observed effects in a study with incentives and a high follow-up rate probably represent what can be expected if some people participate who are not willing to complete the intervention per se. Indeed, limiting research participation to those interested enough in the intervention to go through time-consuming research procedures without being compensated for it is likely to attract individuals who are more interested in the intervention than the general public. In the long term, we think that the risk of overestimating intervention effects is more problematic than the use of incentives (at least those used in our study). It is our opinion that no study should sacrifice low attrition rates. The impact of incentives on attrition rates and the profile of participants in the specific context of internet trials could be studied to determine whether and how it may influence the study results, whether different forms of incentives (money, coupons) have different effects and whether these effects may differ by culture and countries. The present letter addresses comments made on a study conducted by N.B., B.B. and J.A.C.
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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».