Impact in Participatory Health Research
Bibliographic record
Abstract
The idea for this special issue arose from the first International Scientific Meeting on the Impact of Participatory Health Research organized by the International Collaboration\nfor Participatory Health Research (ICPHR), the German Network for Participatory Health Research (PartNet), the Institute of Population and PublicHealth,Canadian Institutes\nofHealth Research (CIHR), and Community-Based Research Canada (CBRC). The conference took place in June 2015 at the Center for Interdisciplinary Research (ZiF) in Bielefeld, Germany. Experts in PHR from eleven countries met to launch an international discussion on what impact means in the participatory research process, how to maximize the impact of the research, and how to observe and document what impact has occurred. Several of the themes discussed at the conference are addressed in this issue.
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 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 | 0.106 | 0.176 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.017 | 0.028 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".