Commentary on Verdejo‐Garcia <i>et al</i>.: Cognitive training and remediation: identifying priorities for intervention research
Notice bibliographique
Résumé
The perspectives of youth, adults and family with lived experience and key knowledge users such as health-care providers and decision-makers are critical to further inform these priorities for cognitive intervention research. Verdejo-Garcia and colleagues [1] outline a large-scale and collaborative effort to identify research priorities to address cognitive deficits in substance use disorders. This commentary outlines additional considerations to ensure that strategic and coordinated research priorities have a meaningful impact upon health systems. Put most clearly, the input of youth, adults and family with lived experience and key knowledge users such as health-care providers and decision-makers is critical for cognitive intervention research with impact. As long noted in health science research, the substantial gaps between research and practice require greater connection between academic and knowledge user groups. Increased connectedness between researchers and knowledge users facilitates knowledge mobilization [2] and research impact [3]. It is increasingly considered essential to engage knowledge users in all stages of research focusing upon issues relevant to them, to improve research quality and relevance [4]. Frameworks have been developed to support and advocate for this approach, including Canada's Strategy for Patient-Oriented Research (SPOR) Framework [5], the United Kingdom's National Institute for Health Research [6] and the American Patient-Centered Outcomes Research Network [7]. Substance use research is commonly conducted with limited to no involvement of people who use substances; this practice has the potential not only to fail to address key issues but also to even intensify stigma and marginalization [8, 9]. Verdejo-Garcia and colleagues highlight the potential for the identified consensus to provide a roadmap to direct future participatory research; however, the incorporation of knowledge users to inform the roadmap itself holds considerable value. Indeed, frameworks for engagement such as those noted above recognize the skills and expertise that stakeholders can bring to research and decision-making processes [10]. Research guidelines and frameworks developed by and for people who use substances are available to promote more effective research partnerships at this early stage and may be particularly helpful, as other guidance materials can emphasize health-care providers and decision-makers rather than those with lived experience or the broader community [11]. Recently published key issues in the field further underscore the key importance of engagement from the very beginning of the research process in the substance use field [12]. The input of broad knowledge user groups is particularly important to extend this novel consensus to include harmonized and equitable (a) outcomes and (b) implementation approaches. Regarding outcomes, Verdejo-Garcia and colleagues carefully derive consensus on cognitive targets in cognitive intervention research, and it is likely that primary outcomes would include measures of these targets. However, cognitive intervention research in other domains highlights the importance of functional recovery outcome measures within this research [13, 14]. Indeed, these functional recovery outcomes have greater ecological validity than many cognitive outcomes and often align more closely with patient treatment goals. Such goals are also aligned with long-standing calls in substance use research to go beyond substance use frequency and abstinence as primary outcomes [15]. Nevertheless, those with lived experience as well as health-care providers and decision-makers are critical to determining harmonized outcomes that are relevant and meaningful to health systems and those that access them. Regarding implementation, it is increasingly recognized that nuanced efficacy research must be followed by equally rigorous implementation science research to support the integration of interventions into health systems. Verdejo-Garcia and colleagues do not explicitly endorse a specific approach to this essential phase of research, which would similarly benefit from broad consensus and harmonization. Frameworks such as the Damschroder et al. [16] Consolidated Framework for Implementation Research may support an approach to implementing cognitive interventions globally in a standardized manner and harmonizing implementation outcomes in the future. Consensus upon implementation outcome measures such as acceptability, adoption, appropriateness, cost, feasibility, penetration, sustainability and fidelity is key to a relevant, common and comprehensive list of key outcomes, which may more effectively advance the field and mobilize this knowledge. In summary, health research has long been characterized by a ‘know–do’ gap wherein the results of research frequently fail to be realized in practice and policy [17]. Investigations of cognitive interventions can meaningfully advance both theory and practice. Restructuring health research to prioritize partnerships between researchers and knowledge users, even at the earliest stage of identifying research priorities, is integral to conduct meaningful and impactful research for all [18]. None. There are no financial or other relevant links to companies with an interest in the topic of this article. Lena C. Quilty: Conceptualization (lead); writing—original draft (lead); writing—review and editing (lead).
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 | 0,009 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,003 |
| 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 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 ».