Bibliographic record
Abstract
This issue contains series of articles on patient involvement in research. The challenges of expertise within research and research agenda setting are the focus of the article by Janneke Elberse et al.1 A dialogue meeting attended by experts, patients and patient representatives revealed exclusionary mechanisms used to constrain non-expert voices. They argue that such meetings are important for changing research cultures but need to take account of the inherent power differences of participants. Overcoming such challenges is central to the article by Baart et al.2 who present a Dialogue Model developed in the Netherlands as a result of action research to consider fundamental genomics research with scientists, patients and family members. Central to their approach is the identification of a common basis for the participants to discuss despite their different perspectives. Also in relation to research on genetics, Daudelin et al.3 studied a Canadian science/policy network that integrated a small group of citizens with policy makers and researchers to produce transdisciplinary policy-oriented research and to foster knowledge transfer and exchange. The approach taken by expert members of the network to govern citizen involvement reveals how they conceptualized and understood the role and potential of citizen participation. The impacts of the citizens participating in the network on research were difficult to identify but the experience of being part of the network resulted in the other members being convinced of the benefit of involvement. A different approach to involving teenagers, adult patients with diabetes, parents and professional stakeholder in research using a Stakeholder Action Group is explored in the article by Lowes et al.4 Purposive sampling of participants, an independent facilitator, careful planning and managing expectations are some of the key learning points from this study but as important is the positioning of this learning within the sparse literature on evaluating the impact of patient and public involvement in research. A more formal involvement mechanism in the Australian context, a health service committee (part of the Community Participation Program in New South Wales), is explored in the contribution from Nathan et al.5 The key issue they interrogate is the expectations and response of staff in hospitals in terms of the legitimacy and roles of community representatives. The need for clarification about roles and contestation regarding authenticity and training of representatives emerged as central findings but so did examples of impacts that those involved had made particularly in terms of service re-design and delivery. The next article explores involvement in the development of interventions and how lived experience challenges and refines the content and approach to intervention. Christabel Owens et al.6 report the use of participatory workshops that brought service users and clinicians together to develop appropriate wording for a text-messaging intervention to reduce self-harm. A key impact of the involvement was helping the clinicians to recognize that messages needed to be individualized to make a difference and consequentially co-produce with the participants in the intervention the messages most appropriate to support themselves. The final set of articles report findings based on patient experience and involvement. Lawton et al.7 used vignettes with women with young children in the UK to understand better the balance between outcomes and communication in assessing the quality and safety of ante-natal care and the likelihood of lodging a complaint. McHorney and Spain document the reasons for not refilling prescriptions or continuing with medication given by a sample of people in the United States with chronic conditions.8 The awareness of respiratory patients of changes in local service provision in England and Wales, and the attitudes of health professionals to their involvement, is explored by Kielmann et al.9 Taken together, these articles illustrate the diverse ways that involvement by different kinds of patients and health-care professionals in different contexts and dealing with different types of health-care issues is becoming a more generally accepted practice and a way of generating better health care. The next issue will include the second Health Policy Digest looking at approaches to patient and public involvement and shared decision making in the Nordic countries. I would be happy to take suggestions for future policy digests and look forward to comments from readers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.015 |
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; both teacher heads agree on what is shown here.
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".