The measure of meaning: redefining success in patient-oriented research
Bibliographic record
Abstract
Purpose This manuscript aims to consider traditional success metrics in patient-oriented research (POR) using insights from the Helping Enable Access and Remove Barriers To Support for Young Adults with Mental Health-Related Disabilities (HEARTS) study. Design/methodology/approach Through collective reflexivity, this manuscript underscores the inadequacy of current evaluation standards that focus primarily on quantifiable outputs. Findings The findings suggest that significant systemic challenges persist, including ageism and discrimination, which undermine the efforts of POR. Practical implications This manuscript argues for an expanded evaluation encompassing traditional metrics and integrating emotional, experiential and community impact measures. Such an approach is crucial to capturing POR's comprehensive effects and fostering a research environment that values inclusivity, supports well-being and ensures responsive and equitable research practices. Thus, aligning with the transformative goals of POR, aiming to enhance the quality and impact of health research and reflect the profound personal and communal transformations that are as significant as the outcomes they facilitate. Originality/value This manuscript represents an emancipatory approach to POR, distinguished by its authentic co-authorship model. Uniquely, it is composed in collaboration with young adults who are experts in experience and coresearchers. These co-authors bring invaluable first-hand insights that both critique and enrich our understanding, enabling them to actively shape the discourse and direction of future POR research. This collaboration ensures the development of more relevant, grounded and transformative approaches in mental health research, thereby enhancing the pertinence and impact of these findings in real-world settings.
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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 | 0.319 | 0.461 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.008 | 0.058 |
| Scholarly communication | 0.029 | 0.032 |
| Open science | 0.003 | 0.030 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".