Early-career researchers’ views on ethical dimensions of patient engagement in research
Bibliographic record
Abstract
BACKGROUND: Increasing attention and efforts are being put towards engaging patients in health research, and some have even argued that patient engagement in research (PER) is an ethical imperative. Yet there is relatively little empirical data on ethical issues associated with PER. METHODS: A three-round Delphi survey was conducted with a panel of early-career researchers (ECRs) involved in PER. One of the objectives was to examine the ethical dimensions of PER as well as ECRs' self-perceived level of preparedness to conduct PER ethically. The study was conducted among awardees of the Québec SPOR-SUPPORT Unit in Canada, who represent the next generation of researchers involved in PER. Many themes were addressed throughout the study, such as definition, values, patients' roles, expected characteristics of patients, and anticipated challenges (including ethical issues). Open-ended questions were used, and all quantitative data were collected through statements using 7-point Likert scales. RESULTS: Between April and November 2016, 25 ECRs were invited to participate; 18 completed both the first and second rounds, and 16 completed the third round. Panelists consisted of nine women and seven men with various backgrounds (general practitioners and postgraduate students). The majority were between 25 and 44 years old. Panelists' responses showed PER raises important ethical issues: 1) professionalization of patients involved in research (with risks of patients becoming less representative); 2) adequate remuneration of patients; 3) fair recognition of patients' experiential knowledge; and 4) tokenism (engaging patients only for symbolic appeal). While the panelists felt moderately prepared to confront these ethical issues, they reported being uncomfortable applying for an ethics certificate for a PER project. CONCLUSION: If PER is an ethical imperative, it is vital to establish clear ethical standards and to train and support the PER community to identify and resolve ethical issues. Despite their overall readiness to conduct PER, panelists did not feel adequately prepared to address many of these issues. It is not easy for ECRs to reconcile ethical desiderata and logistical imperatives. Additional research should focus on supporting the responsible conduct of PER, which, if not done, can undermine the credibility and feasibility of the entire PER enterprise.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.199 | 0.201 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier 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".