Commentary on Verdejo‐Garcia <i>et al</i>.: Cognitive training and remediation: identifying priorities for intervention research
Bibliographic record
Abstract
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".