Activities and impacts of patient engagement in CIHR SPOR funded research: a cross-sectional survey of academic researcher and patient partner experiences
Bibliographic record
Abstract
BACKGROUND: Knowledge about the specific engagement activities pursued and associated impacts of patient engagement in research in Canada remains nascent. This study aimed to describe engagement activities and perceived impacts of projects funded by the Strategy for Patient-Oriented Research (SPOR). METHODS: This was a cross-sectional online survey of academic researchers and patient partners engaging in projects funded through 13 SPOR funding calls (2014-2019). Patient engagement activities and impacts were measured using a self-developed survey. Thematic analysis was used to describe engagement activities and impacts. RESULTS: 66 of 511 academic researchers and 20 of 28 patient partners contacted completed the survey and were included in analyses. Respondents reported that patient partners were engaged in seven types of activities across the research cycle: (a) sharing experiences/giving advice, (b) identifying the research focus/methods, (c) developing/revising aspects of the project, (d) conducting research activities, (e) study participation, (f) presenting on behalf of the project, and (g) other grant development or knowledge translation activities. Engagement was associated with six different types of impacts related to knowledge, outputs, or directions being (a) created, (b) moulded, (c) confirmed, or (d) chosen/prioritized, (e) perceived success of the research, and (f) minimal/negative impacts on the research. CONCLUSIONS: This study presents information on different ways that patient partners were engaged in SPOR-funded research and the potential impacts of these activities. This knowledge base is imperative to the future of patient engagement in research, including the planning and evaluation of future studies that engage patients as active shapers of research.
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 | 0.020 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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; 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".