Best practice guidelines for citizen science in mental health research: systematic review and evidence synthesis
Pourquoi ce travail est dans la base
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Notice bibliographique
Résumé
Partnering with people most affected by mental health problems can transform mental health outcomes. Citizen science as a research approach enables partnering with the public at a substantial scale, but there is scarce guidance on its use in mental health research. To develop best practise guidelines for conducting and reporting research, we conducted a systematic review of studies reporting mental health citizen science research. Documents were identified from electronic databases ( n = 10), grey literature, conference proceedings, hand searching of specific journals and citation tracking. Document content was organised in NVIVO using the ten European Citizen Science Association (ECSA) citizen science principles. Best practise guidelines were developed by (a) identifying approaches specific to mental health research or where citizen science and mental health practises differ, (b) identifying relevant published reporting guidelines and methodologies already used in mental health research, and (c) identifying specific elements to include in reporting studies. A total of 14,063 documents were screened. Nine studies were included, from Australia, Belgium, Canada, Denmark, Netherlands, Spain, the UK, and the United States. Citizen scientists with lived experience of mental health problems were involved in data collection, analysis, project design, leadership, and dissemination of results. Most studies reported against some ECSA principles but reporting against these principles was often unclear and unstated. Best practise guidelines were developed, which identified mental health-specific issues relevant to citizen science, and reporting recommendations. These included citizen science as a mechanism for empowering people affected by mental health problems, attending to safeguarding issues such as health-related advice being shared between contributors, the use of existing health research reporting guidelines, evaluating the benefits for contributors and impact on researchers, explicit reporting of participation at each research stage, naming the citizen science platform and data repository, and clear reporting of consent processes, data ownership, and data sharing arrangements. We conclude that citizen science is feasible in mental health and can be complementary to other participatory approaches. It can contribute to active involvement, engagement, and knowledge production with the public. The proposed guidelines will support the quality of citizen science reporting.
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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 | 0,061 | 0,094 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,004 | 0,000 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,002 |
| 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 découle