Bibliographic record
Abstract
Welcome to this edition of Health Expectations. The Editors have been discussing what we mean by our strapline and that HEX is ‘an International Journal of Public Participation in Health Care and Health Policy’. We are certainly international, attracting papers from around the world (in this issue, Canada, Israel, Spain, Taiwan as well as USA and UK) and have an international readership. However, there continues to be debate about what we really mean by publication participation. In the review article, Staley et al. reflect on the nature of the evidence that has been published to date, and explore the strengths and weaknesses of the different approaches that have been taken to evaluating the impact of public involvement on research. Using a realistic evaluation, the authors use two previously reported studies (exploring the impact of peer interviewers) and attempt to identify the links between context, mechanism and outcome in public involvement in research. Gagnon et al. make a plea for experimenting with different patient involvement strategies, and assessing their impact, to provide evidence that will inform future work, and highlighting the need to develop a common vocabulary. Table 3 in this paper makes useful reading to any researcher or policymaker planning patient and public involvement in their work. Chen et al. describe an interesting and perhaps unexpected consequence of increased involvement in decision-making – a tendency towards a higher risk of cancer death. The authors reflect on the limitations of this early study, but for clinicians, the challenge is how to ensure all relevant information about options are choices available is given to patients to support shared decision-making.1 Rapaport and colleagues illustrate the complexity of using a decision aid in the area of pre-natal testing, whilst attempting to achieve shared decision-making (SDM), which they describe as an iterative process, designed to find the best treatment for a specific patient through a better understanding of patient preferences by the physician and a better understanding of the medical situation by the patient. Using a decision aid, which is modelled on a biomedical approach and making certain presumptions about how people will behave, can challenge a SDM approach. The framework they present is clear and enables those of us in healthcare systems which are free to the patient at the point of delivery to recognize the extra layer of complexity patient charges present. Rochon and colleagues highlight a further barrier to the use of decision aids: the technology itself. In their paper, the participants were older people, but patients with low health literacy are likely to face similar problems in using decision aids2 or participating in health care about management of long-term conditions.3 Qualitative studies report patients’ views on splenectomy for idiopathic thrombocytopenic purpura, and the role of the community pharmacist, whilst an online survey showed that even academics may not understand what is meant by a ‘family history of cancer’. Such approaches shed light on the many and varied perspectives of patients and the public, and the value of exploring these perspectives in health care and policy. The breadth of the topics in this issue, the methodologies reported, and the implications for policy, practice and research, reflect the different ways in which researchers can explore the patient perspective, but we would like to take this further in our journal. Thus, the Editorial team would like to recruit PPI expertise on the Editorial Board and would encourage anyone who is interested, to contact the team for further discussion. We also wish to encourage short reports from Service User, Research User or Patient and Public Involvement (PPI) groups describing innovative ways in which patient and public involvement has either been researched or has impacted on health care, policy or research. We look forward to receiving your contributions.
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| 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".