From Individuals to Systems and Contributions to Creations: Novel Framework for Mapping the Efforts of Individuals by Convening The Center of Health and Health Care
Bibliographic record
Abstract
BACKGROUND: People with lived health care experiences (often referred to as "patients") are increasingly contributing to health care and are most effective when they are involved as partners who can contribute complementary knowledge alongside other stakeholders in health care. OBJECTIVE: Convening The Center aimed to bring together "people known as patients"-the center of health care-to address priorities as they defined them. METHODS: According to the original project design, an in-person gathering was to be conducted; however, as a result of the COVID-19 pandemic, the in-person gathering was transformed into a series of digital gatherings, including an in-depth interview phase, small-group gatherings, and a collective convening of 25 participants (22 women and 3 men from the United States, India, Costa Rica, Sweden, and Pakistan). Each participant was interviewed on Zoom (Zoom Video Communications Inc), and the interview data were thematically analyzed to design a subsequent small group and then full cohort Zoom sessions. Visual note-taking was used to reinforce a shared understanding of each individual- and group-level conversation. RESULTS: The interviews and gatherings for Convening The Center offered unique perspectives on patient activities in research, health innovation, and problem-solving. This project further developed a novel, two-spectrum framework for assessing different experiences that patients may have or seek to gain, based on what patients actually do, and different levels of patients' involvement, ranging from individual to community to systemic involvement. CONCLUSIONS: The descriptors of patients in academic literature typically focus on what health care providers think patients "are" rather than on what patients "do." The primary result of this project is a framework for mapping what patients "do" and "where" they do their work along two spectra: from creating their own projects to contributing to work initiated by others and from working at levels ranging from individual to community to systems. A better understanding of these spectra may enable researchers to more effectively engage and leverage patient expertise in health care research and innovation.
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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 | 0.024 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.019 | 0.083 |
| Scholarly communication | 0.021 | 0.031 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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; a candidate call from one source (direct Gemma or distilled Codex), 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".